Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations7635
Missing cells81426
Missing cells (%)30.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory280.0 B

Variable types

Categorical18
Text16
Numeric1

Alerts

address is highly overall correlated with certification_providers and 9 other fieldsHigh correlation
age_requirement is highly overall correlated with educationaL_requirements and 5 other fieldsHigh correlation
certification_providers is highly overall correlated with address and 9 other fieldsHigh correlation
certification_skills is highly overall correlated with address and 9 other fieldsHigh correlation
company_urls is highly overall correlated with address and 6 other fieldsHigh correlation
educationaL_requirements is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
experiencere_requirement is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
expiry_dates is highly overall correlated with address and 8 other fieldsHigh correlation
extra_curricular_organization_links is highly overall correlated with address and 8 other fieldsHigh correlation
issue_dates is highly overall correlated with address and 9 other fieldsHigh correlation
languages is highly overall correlated with address and 9 other fieldsHigh correlation
online_links is highly overall correlated with address and 7 other fieldsHigh correlation
proficiency_levels is highly overall correlated with address and 9 other fieldsHigh correlation
responsibilities is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
responsibilities.1 is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
result_types is highly overall correlated with address and 6 other fieldsHigh correlation
skills_required is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
job_position_name is highly overall correlated with age_requirement and 5 other fieldsHigh correlation
address has 7002 (91.7%) missing values Missing
career_objective has 3841 (50.3%) missing values Missing
extra_curricular_activity_types has 4903 (64.2%) missing values Missing
extra_curricular_organization_names has 4903 (64.2%) missing values Missing
extra_curricular_organization_links has 4903 (64.2%) missing values Missing
role_positions has 4903 (64.2%) missing values Missing
languages has 7066 (92.5%) missing values Missing
proficiency_levels has 7066 (92.5%) missing values Missing
certification_providers has 6048 (79.2%) missing values Missing
certification_skills has 6048 (79.2%) missing values Missing
online_links has 6048 (79.2%) missing values Missing
issue_dates has 6048 (79.2%) missing values Missing
expiry_dates has 6048 (79.2%) missing values Missing
experiencere_requirement has 1086 (14.2%) missing values Missing
age_requirement has 3264 (42.8%) missing values Missing
skills_required has 1371 (18.0%) missing values Missing
address is uniformly distributed Uniform
responsibilities is uniformly distributed Uniform
job_position_name is uniformly distributed Uniform
responsibilities.1 is uniformly distributed Uniform
skills_required is uniformly distributed Uniform

Reproduction

Analysis started2025-01-03 09:55:47.789167
Analysis finished2025-01-03 09:55:57.359703
Duration9.57 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

address
Categorical

High correlation  Missing  Uniform 

Distinct28
Distinct (%)4.4%
Missing7002
Missing (%)91.7%
Memory size59.8 KiB
Greer, SC
 
27
Denver, CO
 
26
Portland, OR
 
25
P.O BOX 2200 ELDORET
 
25
Hoboken, NJ
 
25
Other values (23)
505 

Length

Max length110
Median length50
Mean length28.7109
Min length9

Characters and Unicode

Total characters18174
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunnyvale, CA
2nd rowSunnyvale, CA
3rd rowTrenton, NJ
4th row10 King's College Road, Rm. 3302 Toronto, Ontario, M5S 3G4 Canada
5th rowCity, State Zip Code

Common Values

ValueCountFrequency (%)
Greer, SC 27
 
0.4%
Denver, CO 26
 
0.3%
Portland, OR 25
 
0.3%
P.O BOX 2200 ELDORET 25
 
0.3%
Hoboken, NJ 25
 
0.3%
139 Alder Drive, Mississaugua, Ontario, L5N 6P1 25
 
0.3%
Street name, City, YO1 5DD 24
 
0.3%
10 King's College Road, Rm. 3302 Toronto, Ontario, M5S 3G4 Canada 24
 
0.3%
Sunnyvale, CA 24
 
0.3%
1234 Thundar Lane S, Fargo, ND 58102 23
 
0.3%
Other values (18) 385
 
5.0%
(Missing) 7002
91.7%

Length

2025-01-03T09:55:57.576292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 91
 
3.1%
university 68
 
2.3%
street 67
 
2.3%
city 66
 
2.2%
ontario 49
 
1.7%
canada 47
 
1.6%
nj 46
 
1.6%
drive 46
 
1.6%
computer 45
 
1.5%
science 45
 
1.5%
Other values (101) 2379
80.7%

Most occurring characters

ValueCountFrequency (%)
2316
 
12.7%
e 1391
 
7.7%
, 997
 
5.5%
a 931
 
5.1%
r 888
 
4.9%
n 883
 
4.9%
t 827
 
4.6%
i 787
 
4.3%
o 701
 
3.9%
C 431
 
2.4%
Other values (52) 8022
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2316
 
12.7%
e 1391
 
7.7%
, 997
 
5.5%
a 931
 
5.1%
r 888
 
4.9%
n 883
 
4.9%
t 827
 
4.6%
i 787
 
4.3%
o 701
 
3.9%
C 431
 
2.4%
Other values (52) 8022
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2316
 
12.7%
e 1391
 
7.7%
, 997
 
5.5%
a 931
 
5.1%
r 888
 
4.9%
n 883
 
4.9%
t 827
 
4.6%
i 787
 
4.3%
o 701
 
3.9%
C 431
 
2.4%
Other values (52) 8022
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2316
 
12.7%
e 1391
 
7.7%
, 997
 
5.5%
a 931
 
5.1%
r 888
 
4.9%
n 883
 
4.9%
t 827
 
4.6%
i 787
 
4.3%
o 701
 
3.9%
C 431
 
2.4%
Other values (52) 8022
44.1%

career_objective
Text

Missing 

Distinct171
Distinct (%)4.5%
Missing3841
Missing (%)50.3%
Memory size59.8 KiB
2025-01-03T09:55:57.984681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length1425
Median length259
Mean length226.45809
Min length26

Characters and Unicode

Total characters859182
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBig data analytics working and database warehouse manager with robust experience in handling all kinds of data. I have also used multiple cloud infrastructure services and am well acquainted with them. Currently in search of role that offers more of development.
2nd rowFresher looking to join as a data analyst and junior data scientist. Experienced in creating meaningful data dashboards and evaluation models.
3rd rowTo obtain a position in a fast-paced business office environment, demanding a strong organizational, technical, and interpersonal position utilizing my skills and attributes.
4th rowProfessional accountant with an outstanding work ethic and integrity seeking to make a valuable contribution utilizing strong analytical, organizational, communication, and computer skills.
5th rowTo secure an IT specialist, desktop support, network administration, database administrator, technical support specialist or related position with a growing organization where my Microsoft certification, technical aptitude, networking, Windows and Mac OS, Apple and Android IOS, web development, application development, Linux, Microsoft applications, managing, testing, client support, help desk, technical support, troubleshooting, and leadership skills can benefit those who I work for as well as myself.
ValueCountFrequency (%)
and 7912
 
6.2%
to 4868
 
3.8%
a 4301
 
3.4%
in 3484
 
2.7%
i 2690
 
2.1%
the 2461
 
1.9%
of 2426
 
1.9%
with 2311
 
1.8%
data 2255
 
1.8%
learning 2234
 
1.7%
Other values (1219) 92799
72.6%
2025-01-03T09:55:58.822564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123971
14.4%
e 81424
 
9.5%
n 69509
 
8.1%
a 62938
 
7.3%
i 61133
 
7.1%
t 53070
 
6.2%
o 51283
 
6.0%
r 41286
 
4.8%
s 38692
 
4.5%
l 32491
 
3.8%
Other values (64) 243385
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 859182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
123971
14.4%
e 81424
 
9.5%
n 69509
 
8.1%
a 62938
 
7.3%
i 61133
 
7.1%
t 53070
 
6.2%
o 51283
 
6.0%
r 41286
 
4.8%
s 38692
 
4.5%
l 32491
 
3.8%
Other values (64) 243385
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 859182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
123971
14.4%
e 81424
 
9.5%
n 69509
 
8.1%
a 62938
 
7.3%
i 61133
 
7.1%
t 53070
 
6.2%
o 51283
 
6.0%
r 41286
 
4.8%
s 38692
 
4.5%
l 32491
 
3.8%
Other values (64) 243385
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 859182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
123971
14.4%
e 81424
 
9.5%
n 69509
 
8.1%
a 62938
 
7.3%
i 61133
 
7.1%
t 53070
 
6.2%
o 51283
 
6.0%
r 41286
 
4.8%
s 38692
 
4.5%
l 32491
 
3.8%
Other values (64) 243385
28.3%

skills
Text

Distinct340
Distinct (%)4.5%
Missing43
Missing (%)0.6%
Memory size59.8 KiB
2025-01-03T09:55:59.266600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3104
Median length477
Mean length370.9053
Min length2

Characters and Unicode

Total characters2815913
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++', 'Data Structures', 'DBMS', 'RDBMS', 'Informatica', 'Talend', 'Amazon Redshift', 'Microsoft Azure']
2nd row['Data Analysis', 'Data Analytics', 'Business Analysis', 'R', 'SAS', 'PowerBi', 'Tableau', 'Data Visualization', 'Business Analytics', 'Machine Learning']
3rd row['Software Development', 'Machine Learning', 'Deep Learning', 'Risk Assessment', 'Requirement Gathering', 'Application Support', 'JavaScript', 'Python', 'Docker', 'HTML', 'Hive', 'CSS', 'C', 'C++']
4th row['accounts payables', 'accounts receivables', 'Accounts Payable', 'Accounts Receivable', 'administrative functions', 'trial balance', 'banking', 'budget', 'bi', 'closing', 'Computer Applications', 'Credit', 'clients', 'Customer Service', 'data entry', 'delivery', 'driving', 'email', 'insurance', 'inventory', 'ledger', 'Access', 'Excel', 'Outlook', 'PowerPoint', 'Word', 'mortgage loan', 'Enterprise', 'policies', 'QuickBooks', 'Sales', 'sales reports', 'telecommunications', 'phone', 'workflow', 'written']
5th row['Analytical reasoning', 'Compliance testing knowledge', 'Effective time management', 'Public and private accounting', 'accounting', 'accounting systems', 'accounts payable', 'accounts receivable', 'administrative', 'AR', 'billing', 'closing', 'client', 'clients', 'documentation', 'financial', 'financial reports', 'preparation of financial reports', 'Preparation of financial statements', 'fixed assets', 'managing', 'month-end closing', 'policies', 'maintain records', 'reporting', 'Research', 'sales', 'tax', 'taxes', 'tax returns', 'annual reports', 'year-end']
ValueCountFrequency (%)
data 6752
 
2.4%
management 4977
 
1.7%
and 4604
 
1.6%
learning 4292
 
1.5%
analysis 4180
 
1.5%
microsoft 3171
 
1.1%
python 3098
 
1.1%
machine 2926
 
1.0%
office 2676
 
0.9%
financial 2269
 
0.8%
Other values (2561) 248064
86.4%
2025-01-03T09:56:00.064064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 329711
 
11.7%
279417
 
9.9%
e 200034
 
7.1%
n 169862
 
6.0%
i 165867
 
5.9%
, 164593
 
5.8%
a 161999
 
5.8%
t 142933
 
5.1%
o 121299
 
4.3%
s 119461
 
4.2%
Other values (72) 960737
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2815913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 329711
 
11.7%
279417
 
9.9%
e 200034
 
7.1%
n 169862
 
6.0%
i 165867
 
5.9%
, 164593
 
5.8%
a 161999
 
5.8%
t 142933
 
5.1%
o 121299
 
4.3%
s 119461
 
4.2%
Other values (72) 960737
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2815913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 329711
 
11.7%
279417
 
9.9%
e 200034
 
7.1%
n 169862
 
6.0%
i 165867
 
5.9%
, 164593
 
5.8%
a 161999
 
5.8%
t 142933
 
5.1%
o 121299
 
4.3%
s 119461
 
4.2%
Other values (72) 960737
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2815913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 329711
 
11.7%
279417
 
9.9%
e 200034
 
7.1%
n 169862
 
6.0%
i 165867
 
5.9%
, 164593
 
5.8%
a 161999
 
5.8%
t 142933
 
5.1%
o 121299
 
4.3%
s 119461
 
4.2%
Other values (72) 960737
34.1%
Distinct328
Distinct (%)4.3%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
2025-01-03T09:56:00.637345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length212
Median length91
Mean length48.523633
Min length8

Characters and Unicode

Total characters367518
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['The Amity School of Engineering & Technology (ASET), Noida']
2nd row['Delhi University - Hansraj College', 'Delhi University - Hansraj College']
3rd row['Birla Institute of Technology (BIT), Ranchi']
4th row['Martinez Adult Education, Business Training Center ï¼ City , State']
5th row['Kent State University']
ValueCountFrequency (%)
university 6534
 
15.3%
of 3679
 
8.6%
college 2419
 
5.7%
technology 1150
 
2.7%
institute 1140
 
2.7%
school 878
 
2.1%
state 830
 
1.9%
engineering 567
 
1.3%
and 479
 
1.1%
high 433
 
1.0%
Other values (638) 24668
57.7%
2025-01-03T09:56:01.594170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35203
 
9.6%
e 24868
 
6.8%
i 24733
 
6.7%
' 24625
 
6.7%
n 20308
 
5.5%
a 17323
 
4.7%
o 17211
 
4.7%
t 17067
 
4.6%
r 15986
 
4.3%
l 12665
 
3.4%
Other values (57) 157529
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 367518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35203
 
9.6%
e 24868
 
6.8%
i 24733
 
6.7%
' 24625
 
6.7%
n 20308
 
5.5%
a 17323
 
4.7%
o 17211
 
4.7%
t 17067
 
4.6%
r 15986
 
4.3%
l 12665
 
3.4%
Other values (57) 157529
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 367518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35203
 
9.6%
e 24868
 
6.8%
i 24733
 
6.7%
' 24625
 
6.7%
n 20308
 
5.5%
a 17323
 
4.7%
o 17211
 
4.7%
t 17067
 
4.6%
r 15986
 
4.3%
l 12665
 
3.4%
Other values (57) 157529
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 367518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35203
 
9.6%
e 24868
 
6.8%
i 24733
 
6.7%
' 24625
 
6.7%
n 20308
 
5.5%
a 17323
 
4.7%
o 17211
 
4.7%
t 17067
 
4.6%
r 15986
 
4.3%
l 12665
 
3.4%
Other values (57) 157529
42.9%
Distinct180
Distinct (%)2.4%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
2025-01-03T09:56:02.068811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length472
Median length125
Mean length30.597703
Min length6

Characters and Unicode

Total characters231747
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['B.Tech']
2nd row['B.Sc (Maths)', 'M.Sc (Science) (Statistics)']
3rd row['B.Tech']
4th row['Computer Applications Specialist Certificate Program']
5th row['Bachelor of Business Administration']
ValueCountFrequency (%)
of 3740
 
13.7%
science 2876
 
10.6%
bachelor 2479
 
9.1%
b.tech 2167
 
8.0%
master 805
 
3.0%
business 620
 
2.3%
diploma 597
 
2.2%
administration 500
 
1.8%
degree 481
 
1.8%
computer 432
 
1.6%
Other values (232) 12543
46.0%
2025-01-03T09:56:03.319779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 24471
 
10.6%
e 19917
 
8.6%
19666
 
8.5%
c 14772
 
6.4%
o 12018
 
5.2%
i 10730
 
4.6%
n 9257
 
4.0%
a 8928
 
3.9%
B 8354
 
3.6%
r 8129
 
3.5%
Other values (65) 95505
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 24471
 
10.6%
e 19917
 
8.6%
19666
 
8.5%
c 14772
 
6.4%
o 12018
 
5.2%
i 10730
 
4.6%
n 9257
 
4.0%
a 8928
 
3.9%
B 8354
 
3.6%
r 8129
 
3.5%
Other values (65) 95505
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 24471
 
10.6%
e 19917
 
8.6%
19666
 
8.5%
c 14772
 
6.4%
o 12018
 
5.2%
i 10730
 
4.6%
n 9257
 
4.0%
a 8928
 
3.9%
B 8354
 
3.6%
r 8129
 
3.5%
Other values (65) 95505
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 24471
 
10.6%
e 19917
 
8.6%
19666
 
8.5%
c 14772
 
6.4%
o 12018
 
5.2%
i 10730
 
4.6%
n 9257
 
4.0%
a 8928
 
3.9%
B 8354
 
3.6%
r 8129
 
3.5%
Other values (65) 95505
41.2%
Distinct149
Distinct (%)2.0%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
2025-01-03T09:56:03.788691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length121
Median length71
Mean length13.737391
Min length6

Characters and Unicode

Total characters104047
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['2019']
2nd row['2015', '2018']
3rd row['2018']
4th row['2008']
5th row[None]
ValueCountFrequency (%)
2019 1711
 
12.9%
n/a 1241
 
9.3%
2020 1210
 
9.1%
none 781
 
5.9%
2018 615
 
4.6%
2017 598
 
4.5%
2021 455
 
3.4%
2010 445
 
3.3%
2014 396
 
3.0%
2004 320
 
2.4%
Other values (77) 5527
41.6%
2025-01-03T09:56:04.734228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 23466
22.6%
0 13427
12.9%
2 11502
11.1%
1 7612
 
7.3%
[ 7574
 
7.3%
] 7574
 
7.3%
5725
 
5.5%
, 4962
 
4.8%
9 3908
 
3.8%
N 2022
 
1.9%
Other values (40) 16275
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 23466
22.6%
0 13427
12.9%
2 11502
11.1%
1 7612
 
7.3%
[ 7574
 
7.3%
] 7574
 
7.3%
5725
 
5.5%
, 4962
 
4.8%
9 3908
 
3.8%
N 2022
 
1.9%
Other values (40) 16275
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 23466
22.6%
0 13427
12.9%
2 11502
11.1%
1 7612
 
7.3%
[ 7574
 
7.3%
] 7574
 
7.3%
5725
 
5.5%
, 4962
 
4.8%
9 3908
 
3.8%
N 2022
 
1.9%
Other values (40) 16275
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 23466
22.6%
0 13427
12.9%
2 11502
11.1%
1 7612
 
7.3%
[ 7574
 
7.3%
] 7574
 
7.3%
5725
 
5.5%
, 4962
 
4.8%
9 3908
 
3.8%
N 2022
 
1.9%
Other values (40) 16275
15.6%
Distinct78
Distinct (%)1.0%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
2025-01-03T09:56:05.360409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length102
Median length73
Mean length12.611434
Min length6

Characters and Unicode

Total characters95519
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['N/A']
2nd row['N/A', 'N/A']
3rd row['N/A']
4th row[None]
5th row['3.84']
ValueCountFrequency (%)
n/a 7016
50.0%
none 3172
22.6%
4.0 204
 
1.5%
a 144
 
1.0%
3.7 141
 
1.0%
3.8 119
 
0.8%
3.5 117
 
0.8%
cum 105
 
0.7%
laude 105
 
0.7%
3.6 94
 
0.7%
Other values (88) 2808
20.0%
2025-01-03T09:56:06.322927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 18639
19.5%
N 10188
10.7%
[ 7574
7.9%
] 7574
7.9%
A 7250
 
7.6%
/ 7206
 
7.5%
6451
 
6.8%
, 4988
 
5.2%
n 3854
 
4.0%
e 3833
 
4.0%
Other values (52) 17962
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 18639
19.5%
N 10188
10.7%
[ 7574
7.9%
] 7574
7.9%
A 7250
 
7.6%
/ 7206
 
7.5%
6451
 
6.8%
, 4988
 
5.2%
n 3854
 
4.0%
e 3833
 
4.0%
Other values (52) 17962
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 18639
19.5%
N 10188
10.7%
[ 7574
7.9%
] 7574
7.9%
A 7250
 
7.6%
/ 7206
 
7.5%
6451
 
6.8%
, 4988
 
5.2%
n 3854
 
4.0%
e 3833
 
4.0%
Other values (52) 17962
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 18639
19.5%
N 10188
10.7%
[ 7574
7.9%
] 7574
7.9%
A 7250
 
7.6%
/ 7206
 
7.5%
6451
 
6.8%
, 4988
 
5.2%
n 3854
 
4.0%
e 3833
 
4.0%
Other values (52) 17962
18.8%

result_types
Categorical

High correlation 

Distinct30
Distinct (%)0.4%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
['N/A']
2704 
['N/A', 'N/A']
1245 
[None]
885 
[None, None]
762 
['GPA']
541 
Other values (25)
1437 

Length

Max length94
Median length66
Mean length11.405466
Min length6

Characters and Unicode

Total characters86385
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[None]
2nd row['N/A', 'N/A']
3rd row['N/A']
4th row[None]
5th row[None]

Common Values

ValueCountFrequency (%)
['N/A'] 2704
35.4%
['N/A', 'N/A'] 1245
16.3%
[None] 885
 
11.6%
[None, None] 762
 
10.0%
['GPA'] 541
 
7.1%
[None, None, None] 247
 
3.2%
['N/A', 'N/A', 'N/A'] 245
 
3.2%
['GPA', 'GPA'] 223
 
2.9%
['N/A', 'N/A', 'N/A', 'N/A'] 88
 
1.2%
['GPA', None] 87
 
1.1%
Other values (20) 547
 
7.2%

Length

2025-01-03T09:56:06.861978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n/a 6686
52.4%
none 4095
32.1%
gpa 1445
 
11.3%
percentage 114
 
0.9%
cgpa 69
 
0.5%
honors 61
 
0.5%
every 44
 
0.3%
quarter 44
 
0.3%
major 23
 
0.2%
highest 22
 
0.2%
Other values (7) 154
 
1.2%

Most occurring characters

ValueCountFrequency (%)
' 16838
19.5%
N 10781
12.5%
A 8244
9.5%
[ 7574
8.8%
] 7574
8.8%
/ 6686
 
7.7%
5183
 
6.0%
, 4940
 
5.7%
e 4723
 
5.5%
n 4358
 
5.0%
Other values (24) 9484
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 16838
19.5%
N 10781
12.5%
A 8244
9.5%
[ 7574
8.8%
] 7574
8.8%
/ 6686
 
7.7%
5183
 
6.0%
, 4940
 
5.7%
e 4723
 
5.5%
n 4358
 
5.0%
Other values (24) 9484
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 16838
19.5%
N 10781
12.5%
A 8244
9.5%
[ 7574
8.8%
] 7574
8.8%
/ 6686
 
7.7%
5183
 
6.0%
, 4940
 
5.7%
e 4723
 
5.5%
n 4358
 
5.0%
Other values (24) 9484
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 16838
19.5%
N 10781
12.5%
A 8244
9.5%
[ 7574
8.8%
] 7574
8.8%
/ 6686
 
7.7%
5183
 
6.0%
, 4940
 
5.7%
e 4723
 
5.5%
n 4358
 
5.0%
Other values (24) 9484
11.0%
Distinct210
Distinct (%)2.8%
Missing61
Missing (%)0.8%
Memory size59.8 KiB
2025-01-03T09:56:07.391257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length260
Median length95
Mean length35.225376
Min length6

Characters and Unicode

Total characters266797
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Electronics']
2nd row['Mathematics', 'Statistics']
3rd row['Electronics/Telecommunication']
4th row['Computer Applications']
5th row['Accounting']
ValueCountFrequency (%)
engineering 2067
 
8.2%
computer 1811
 
7.2%
science 1679
 
6.6%
n/a 1407
 
5.6%
and 1222
 
4.8%
accounting 970
 
3.8%
none 758
 
3.0%
technology 746
 
2.9%
management 736
 
2.9%
business 726
 
2.9%
Other values (232) 13201
52.1%
2025-01-03T09:56:08.583410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 24374
 
9.1%
' 23512
 
8.8%
e 21309
 
8.0%
i 18676
 
7.0%
17749
 
6.7%
c 14486
 
5.4%
o 12445
 
4.7%
t 12413
 
4.7%
a 11934
 
4.5%
r 10179
 
3.8%
Other values (50) 99720
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 266797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 24374
 
9.1%
' 23512
 
8.8%
e 21309
 
8.0%
i 18676
 
7.0%
17749
 
6.7%
c 14486
 
5.4%
o 12445
 
4.7%
t 12413
 
4.7%
a 11934
 
4.5%
r 10179
 
3.8%
Other values (50) 99720
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 266797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 24374
 
9.1%
' 23512
 
8.8%
e 21309
 
8.0%
i 18676
 
7.0%
17749
 
6.7%
c 14486
 
5.4%
o 12445
 
4.7%
t 12413
 
4.7%
a 11934
 
4.5%
r 10179
 
3.8%
Other values (50) 99720
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 266797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 24374
 
9.1%
' 23512
 
8.8%
e 21309
 
8.0%
i 18676
 
7.0%
17749
 
6.7%
c 14486
 
5.4%
o 12445
 
4.7%
t 12413
 
4.7%
a 11934
 
4.5%
r 10179
 
3.8%
Other values (50) 99720
37.4%
Distinct199
Distinct (%)2.6%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:09.310205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length266
Median length135
Mean length49.603066
Min length6

Characters and Unicode

Total characters375396
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['Coca-COla']
2nd row['BIB Consultancy']
3rd row['Axis Bank Limited']
4th row['Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'N/A']
5th row['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name']
ValueCountFrequency (%)
company 14400
31.2%
name 14334
31.0%
of 732
 
1.6%
university 646
 
1.4%
ltd 507
 
1.1%
n/a 388
 
0.8%
377
 
0.8%
pvt 342
 
0.7%
science 255
 
0.6%
and 251
 
0.5%
Other values (425) 13964
30.2%
2025-01-03T09:56:10.652072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 42370
 
11.3%
38628
 
10.3%
a 35296
 
9.4%
m 30614
 
8.2%
e 23308
 
6.2%
o 21459
 
5.7%
n 21347
 
5.7%
C 16795
 
4.5%
y 16380
 
4.4%
p 15892
 
4.2%
Other values (59) 113307
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 42370
 
11.3%
38628
 
10.3%
a 35296
 
9.4%
m 30614
 
8.2%
e 23308
 
6.2%
o 21459
 
5.7%
n 21347
 
5.7%
C 16795
 
4.5%
y 16380
 
4.4%
p 15892
 
4.2%
Other values (59) 113307
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 42370
 
11.3%
38628
 
10.3%
a 35296
 
9.4%
m 30614
 
8.2%
e 23308
 
6.2%
o 21459
 
5.7%
n 21347
 
5.7%
C 16795
 
4.5%
y 16380
 
4.4%
p 15892
 
4.2%
Other values (59) 113307
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 42370
 
11.3%
38628
 
10.3%
a 35296
 
9.4%
m 30614
 
8.2%
e 23308
 
6.2%
o 21459
 
5.7%
n 21347
 
5.7%
C 16795
 
4.5%
y 16380
 
4.4%
p 15892
 
4.2%
Other values (59) 113307
30.2%

company_urls
Categorical

High correlation 

Distinct16
Distinct (%)0.2%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
['N/A']
1944 
[None, None, None]
1164 
[None, None, None, None]
1049 
[None]
1024 
[None, None]
527 
Other values (11)
1860 

Length

Max length277
Median length48
Mean length18.033034
Min length6

Characters and Unicode

Total characters136474
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[None]
2nd row['N/A']
3rd row['N/A']
4th row[None, None, None, None, None, None]
5th row[None, None, None, None, None]

Common Values

ValueCountFrequency (%)
['N/A'] 1944
25.5%
[None, None, None] 1164
15.2%
[None, None, None, None] 1049
13.7%
[None] 1024
13.4%
[None, None] 527
 
6.9%
[None, None, None, None, None] 453
 
5.9%
[None, None, None, None, None, None] 356
 
4.7%
['N/A', 'N/A', 'N/A'] 269
 
3.5%
['N/A', 'N/A'] 262
 
3.4%
[None, None, None, None, None, None, None] 243
 
3.2%
Other values (6) 277
 
3.6%

Length

2025-01-03T09:56:11.046429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 17273
81.4%
n/a 3723
 
17.5%
https://www.calero.com 25
 
0.1%
https://www.clacorp.com 25
 
0.1%
https://www.pictureu.com 25
 
0.1%
https://www.ticketalternative.com 25
 
0.1%
https://www.triplingo.com 25
 
0.1%
https://www.deposco.com 25
 
0.1%
https://www.servigistics.com 25
 
0.1%
https://www.commercescience.com 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 20996
15.4%
o 17673
12.9%
e 17598
12.9%
n 17373
12.7%
, 13653
10.0%
13653
10.0%
' 7896
 
5.8%
[ 7568
 
5.5%
] 7568
 
5.5%
/ 4173
 
3.1%
Other values (19) 8323
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 20996
15.4%
o 17673
12.9%
e 17598
12.9%
n 17373
12.7%
, 13653
10.0%
13653
10.0%
' 7896
 
5.8%
[ 7568
 
5.5%
] 7568
 
5.5%
/ 4173
 
3.1%
Other values (19) 8323
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 20996
15.4%
o 17673
12.9%
e 17598
12.9%
n 17373
12.7%
, 13653
10.0%
13653
10.0%
' 7896
 
5.8%
[ 7568
 
5.5%
] 7568
 
5.5%
/ 4173
 
3.1%
Other values (19) 8323
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 20996
15.4%
o 17673
12.9%
e 17598
12.9%
n 17373
12.7%
, 13653
10.0%
13653
10.0%
' 7896
 
5.8%
[ 7568
 
5.5%
] 7568
 
5.5%
/ 4173
 
3.1%
Other values (19) 8323
 
6.1%
Distinct252
Distinct (%)3.3%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:11.587557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length129
Median length112
Mean length34.998943
Min length6

Characters and Unicode

Total characters264872
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Nov 2019']
2nd row['Sep 2019']
3rd row['June 2018']
4th row['January 2011', 'January 2008', 'January 2006', 'January 2004', 'January 2001', 'N/A']
5th row['January 2016', 'January 2016', 'January 2012', 'January 2009', 'January 2006']
ValueCountFrequency (%)
2019 2032
 
6.1%
january 1982
 
5.9%
2020 1217
 
3.6%
may 1024
 
3.1%
jan 995
 
3.0%
june 749
 
2.2%
2018 729
 
2.2%
august 690
 
2.1%
2011 677
 
2.0%
july 671
 
2.0%
Other values (271) 22778
67.9%
2025-01-03T09:56:12.507204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 42226
15.9%
0 33135
12.5%
25976
 
9.8%
2 21958
 
8.3%
1 18551
 
7.0%
, 13713
 
5.2%
9 9302
 
3.5%
/ 7928
 
3.0%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (40) 76947
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 42226
15.9%
0 33135
12.5%
25976
 
9.8%
2 21958
 
8.3%
1 18551
 
7.0%
, 13713
 
5.2%
9 9302
 
3.5%
/ 7928
 
3.0%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (40) 76947
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 42226
15.9%
0 33135
12.5%
25976
 
9.8%
2 21958
 
8.3%
1 18551
 
7.0%
, 13713
 
5.2%
9 9302
 
3.5%
/ 7928
 
3.0%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (40) 76947
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 42226
15.9%
0 33135
12.5%
25976
 
9.8%
2 21958
 
8.3%
1 18551
 
7.0%
, 13713
 
5.2%
9 9302
 
3.5%
/ 7928
 
3.0%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (40) 76947
29.1%
Distinct246
Distinct (%)3.3%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:13.041003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length133
Median length105
Mean length34.138478
Min length6

Characters and Unicode

Total characters258360
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Till Date']
2nd row['Till Date']
3rd row['Till Date']
4th row['November 2015', 'January 2010', 'January 2008', 'January 2006', 'January 2004', None]
5th row['Current', 'January 2016', 'January 2015', 'January 2011', 'January 2009']
ValueCountFrequency (%)
current 2305
 
7.4%
january 1587
 
5.1%
date 1123
 
3.6%
till 1123
 
3.6%
2020 1076
 
3.4%
may 820
 
2.6%
august 651
 
2.1%
2014 617
 
2.0%
none 572
 
1.8%
ongoing 564
 
1.8%
Other values (233) 20904
66.7%
2025-01-03T09:56:13.924894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 41298
16.0%
0 26636
 
10.3%
23774
 
9.2%
2 18077
 
7.0%
1 13815
 
5.3%
, 13713
 
5.3%
r 9484
 
3.7%
e 9410
 
3.6%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (45) 87017
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 41298
16.0%
0 26636
 
10.3%
23774
 
9.2%
2 18077
 
7.0%
1 13815
 
5.3%
, 13713
 
5.3%
r 9484
 
3.7%
e 9410
 
3.6%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (45) 87017
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 41298
16.0%
0 26636
 
10.3%
23774
 
9.2%
2 18077
 
7.0%
1 13815
 
5.3%
, 13713
 
5.3%
r 9484
 
3.7%
e 9410
 
3.6%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (45) 87017
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 41298
16.0%
0 26636
 
10.3%
23774
 
9.2%
2 18077
 
7.0%
1 13815
 
5.3%
, 13713
 
5.3%
r 9484
 
3.7%
e 9410
 
3.6%
] 7568
 
2.9%
[ 7568
 
2.9%
Other values (45) 87017
33.7%
Distinct298
Distinct (%)3.9%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:14.403828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length1138
Median length394
Mean length179.77762
Min length4

Characters and Unicode

Total characters1360557
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row[['Big Data']]
2nd row[['Data Analysis', 'Business Analysis', 'Machine Learning']]
3rd row[['Unified Payment Interface', 'Risk Prediction', 'Big Data', 'Spark', 'PySpark']]
4th row[['accounts receivables', 'banking', 'G/L Accounts', 'accounts payables', 'credit cards', 'reconcile', 'commission reports', 'credit checks', 'customer service', 'international travel'], ['data entry', 'accounts receivable', 'cash handling', 'customer communication', 'inventory reports', 'problem-solving'], ['mortgage processing', 'analytical aptitude', 'credit reports', 'customer communication'], ['commercial auto underwriting', 'data entry', 'application review', 'customer communication'], ['personal auto underwriting', 'data entry', 'policy review', 'customer service'], ['training', 'medical record review', 'data entry', 'document design', 'customer service', 'team performance']]
5th row[['collections', 'accounts receivable', 'financial reports', 'AR aging', 'customer queries', 'sales and use tax audits'], ['financial statements', 'GAAP', 'asset', 'liability', 'capital account', 'accounting controls', 'audits'], ['sales tax', 'tax returns', 'business licenses', 'annual reports', 'tax audits'], ['financial reporting', 'fixed assets', 'sales tax', 'cash projections', 'general ledger accounting'], ['audit procedures', 'substantive tests', 'internal accounting', 'tests of compliance', 'audit programs']]
ValueCountFrequency (%)
management 4198
 
3.2%
none 3920
 
3.0%
data 2583
 
2.0%
analysis 2363
 
1.8%
and 2037
 
1.6%
financial 2003
 
1.5%
development 1543
 
1.2%
learning 1462
 
1.1%
accounting 1310
 
1.0%
sales 1280
 
1.0%
Other values (1436) 107053
82.5%
2025-01-03T09:56:15.249433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 137357
 
10.1%
122184
 
9.0%
e 104363
 
7.7%
n 98158
 
7.2%
a 83710
 
6.2%
i 81743
 
6.0%
t 75306
 
5.5%
, 66012
 
4.9%
o 64777
 
4.8%
s 54931
 
4.0%
Other values (65) 472016
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1360557
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 137357
 
10.1%
122184
 
9.0%
e 104363
 
7.7%
n 98158
 
7.2%
a 83710
 
6.2%
i 81743
 
6.0%
t 75306
 
5.5%
, 66012
 
4.9%
o 64777
 
4.8%
s 54931
 
4.0%
Other values (65) 472016
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1360557
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 137357
 
10.1%
122184
 
9.0%
e 104363
 
7.7%
n 98158
 
7.2%
a 83710
 
6.2%
i 81743
 
6.0%
t 75306
 
5.5%
, 66012
 
4.9%
o 64777
 
4.8%
s 54931
 
4.0%
Other values (65) 472016
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1360557
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 137357
 
10.1%
122184
 
9.0%
e 104363
 
7.7%
n 98158
 
7.2%
a 83710
 
6.2%
i 81743
 
6.0%
t 75306
 
5.5%
, 66012
 
4.9%
o 64777
 
4.8%
s 54931
 
4.0%
Other values (65) 472016
34.7%
Distinct301
Distinct (%)4.0%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:15.741555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length293
Median length148
Mean length73.203621
Min length7

Characters and Unicode

Total characters554005
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Big Data Analyst']
2nd row['Business Analyst']
3rd row['Software Developer (Machine Learning Engineer)']
4th row['Accountant', 'Accounts Receivable Clerk', 'Mortgage Underwriter', 'Commercial Auto Underwriter', 'Personal Auto Underwriter', 'Claims Examiner']
5th row['Staff Accountant', 'Senior Accountant', 'Tax Analyst', 'Staff Accountant II', 'Staff Auditor II']
ValueCountFrequency (%)
manager 2798
 
5.0%
engineering 2740
 
4.9%
engineer 2488
 
4.4%
accountant 2213
 
4.0%
intern 2104
 
3.8%
technician 1635
 
2.9%
analyst 1544
 
2.8%
assistant 1379
 
2.5%
software 1206
 
2.2%
business 1015
 
1.8%
Other values (489) 36879
65.9%
2025-01-03T09:56:16.711974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 51894
 
9.4%
e 49804
 
9.0%
48433
 
8.7%
' 42398
 
7.7%
i 33814
 
6.1%
a 33046
 
6.0%
t 32326
 
5.8%
r 31795
 
5.7%
o 21520
 
3.9%
c 21194
 
3.8%
Other values (51) 187781
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 554005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 51894
 
9.4%
e 49804
 
9.0%
48433
 
8.7%
' 42398
 
7.7%
i 33814
 
6.1%
a 33046
 
6.0%
t 32326
 
5.8%
r 31795
 
5.7%
o 21520
 
3.9%
c 21194
 
3.8%
Other values (51) 187781
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 554005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 51894
 
9.4%
e 49804
 
9.0%
48433
 
8.7%
' 42398
 
7.7%
i 33814
 
6.1%
a 33046
 
6.0%
t 32326
 
5.8%
r 31795
 
5.7%
o 21520
 
3.9%
c 21194
 
3.8%
Other values (51) 187781
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 554005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 51894
 
9.4%
e 49804
 
9.0%
48433
 
8.7%
' 42398
 
7.7%
i 33814
 
6.1%
a 33046
 
6.0%
t 32326
 
5.8%
r 31795
 
5.7%
o 21520
 
3.9%
c 21194
 
3.8%
Other values (51) 187781
33.9%
Distinct86
Distinct (%)1.1%
Missing67
Missing (%)0.9%
Memory size59.8 KiB
2025-01-03T09:56:17.235163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length152
Median length128
Mean length36.40473
Min length6

Characters and Unicode

Total characters275511
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['N/A']
2nd row['N/A']
3rd row['N/A']
4th row['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'N/A']
5th row['City, State', 'City, State', 'City, State', 'City, State', 'City, State']
ValueCountFrequency (%)
city 13428
32.6%
state 13033
31.6%
4789
 
11.6%
n/a 4555
 
11.1%
none 1168
 
2.8%
ca 163
 
0.4%
fl 111
 
0.3%
remote 109
 
0.3%
tx 109
 
0.3%
lexington 88
 
0.2%
Other values (103) 3637
 
8.8%
2025-01-03T09:56:18.099302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 40705
14.8%
' 40106
14.6%
33622
12.2%
, 28234
10.2%
e 16022
 
5.8%
a 14669
 
5.3%
i 14550
 
5.3%
C 14154
 
5.1%
y 13605
 
4.9%
S 13384
 
4.9%
Other values (44) 46460
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 40705
14.8%
' 40106
14.6%
33622
12.2%
, 28234
10.2%
e 16022
 
5.8%
a 14669
 
5.3%
i 14550
 
5.3%
C 14154
 
5.1%
y 13605
 
4.9%
S 13384
 
4.9%
Other values (44) 46460
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 40705
14.8%
' 40106
14.6%
33622
12.2%
, 28234
10.2%
e 16022
 
5.8%
a 14669
 
5.3%
i 14550
 
5.3%
C 14154
 
5.1%
y 13605
 
4.9%
S 13384
 
4.9%
Other values (44) 46460
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 40705
14.8%
' 40106
14.6%
33622
12.2%
, 28234
10.2%
e 16022
 
5.8%
a 14669
 
5.3%
i 14550
 
5.3%
C 14154
 
5.1%
y 13605
 
4.9%
S 13384
 
4.9%
Other values (44) 46460
16.9%

responsibilities
Categorical

High correlation  Uniform 

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.8 KiB
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting
 
284
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting
 
283
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation
 
281
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation
 
281
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
 
280
Other values (23)
6226 

Length

Max length587
Median length197
Mean length218.03039
Min length72

Characters and Unicode

Total characters1664662
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechnical Support Troubleshooting Collaboration Documentation System Monitoring Software Deployment Training & Mentorship Industry Trends Field Visits
2nd rowMachine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
3rd rowTrade Marketing Executive Brand Visibility, Sales Targets Field Marketing, Campaigns, Product Distribution Brand Head Excel, KPIs Tracking
4th rowApparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication
5th rowiOS Lifecycle Requirement Analysis Native Frameworks iOS Development API Integration Technical Communication UI Design Performance Optimization Feature Collaboration Bug Fixing Code Translation High-Performance Development Task Management Cross-Team Collaboration Code Quality

Common Values

ValueCountFrequency (%)
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting 284
 
3.7%
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting 283
 
3.7%
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation 281
 
3.7%
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation 281
 
3.7%
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis 280
 
3.7%
Mikrotik Router Configuration OLT Device Setup & Management Integration with Billing Software Network Monitoring Tools Integration Connectivity Troubleshooting Technical Support & Escalation Installation & Configuration GPON/EPON Expertise Cisco, OLT, MikroTik Knowledge 279
 
3.7%
Supervision Monitoring Construction Estimation Planning Material Management Project Coordination Quality Assurance Cost Control Inventory Operations Safety Error Escalation Miscellaneous Tasks 277
 
3.6%
Management Trainee Mechanical Systems Maintenance & Troubleshooting Performance Analysis Project Support Process Improvement Training & Development Administrative Support 277
 
3.6%
Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication 276
 
3.6%
Machine Learning Design Data Analysis Model Training AI Integration Innovation Cross-Functional Collaboration Model Deployment Documentation Analytical Skills Communication Team Collaboration 276
 
3.6%
Other values (18) 4841
63.4%

Length

2025-01-03T09:56:18.483585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11385
 
6.0%
management 5457
 
2.9%
development 4132
 
2.2%
design 3876
 
2.0%
data 3809
 
2.0%
collaboration 3574
 
1.9%
support 3024
 
1.6%
analysis 2752
 
1.4%
documentation 2469
 
1.3%
monitoring 2456
 
1.3%
Other values (323) 147881
77.5%

Most occurring characters

ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%
Distinct86
Distinct (%)3.1%
Missing4903
Missing (%)64.2%
Memory size59.8 KiB
2025-01-03T09:56:18.904115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length123
Median length51
Mean length26.225842
Min length6

Characters and Unicode

Total characters71649
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Professional Organization', 'Honor Society', 'Honor Society', 'Honor Society']
2nd row['N/A']
3rd row['Award', 'Competition', 'Competition']
4th row['Competition', 'Competition', 'Membership']
5th row['Scholarship']
ValueCountFrequency (%)
volunteering 441
 
6.6%
course 401
 
6.0%
membership 344
 
5.1%
n/a 203
 
3.0%
competition 181
 
2.7%
club 175
 
2.6%
society 163
 
2.4%
data 163
 
2.4%
workshop 138
 
2.1%
learning 132
 
2.0%
Other values (139) 4351
65.0%
2025-01-03T09:56:20.031568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 7924
 
11.1%
e 5741
 
8.0%
i 4639
 
6.5%
o 4254
 
5.9%
3960
 
5.5%
r 3871
 
5.4%
n 3858
 
5.4%
t 3732
 
5.2%
a 2786
 
3.9%
[ 2732
 
3.8%
Other values (45) 28152
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 7924
 
11.1%
e 5741
 
8.0%
i 4639
 
6.5%
o 4254
 
5.9%
3960
 
5.5%
r 3871
 
5.4%
n 3858
 
5.4%
t 3732
 
5.2%
a 2786
 
3.9%
[ 2732
 
3.8%
Other values (45) 28152
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 7924
 
11.1%
e 5741
 
8.0%
i 4639
 
6.5%
o 4254
 
5.9%
3960
 
5.5%
r 3871
 
5.4%
n 3858
 
5.4%
t 3732
 
5.2%
a 2786
 
3.9%
[ 2732
 
3.8%
Other values (45) 28152
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 7924
 
11.1%
e 5741
 
8.0%
i 4639
 
6.5%
o 4254
 
5.9%
3960
 
5.5%
r 3871
 
5.4%
n 3858
 
5.4%
t 3732
 
5.2%
a 2786
 
3.9%
[ 2732
 
3.8%
Other values (45) 28152
39.3%
Distinct89
Distinct (%)3.3%
Missing4903
Missing (%)64.2%
Memory size59.8 KiB
2025-01-03T09:56:20.472486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length186
Median length99
Mean length31.755857
Min length6

Characters and Unicode

Total characters86757
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['Ohio Society of CPAs', 'Beta Alpha Psi', 'Golden Key International Honour Society', 'Beta Gamma Sigma']
2nd row['N/A']
3rd row['N/A', 'N/A', 'Young Economist Innovation']
4th row['The ECE Society, BIT Mesra, Ranchi', 'SGGSCC, University of Delhi', 'National Service Scheme']
5th row['KSST']
ValueCountFrequency (%)
n/a 934
 
8.9%
of 447
 
4.3%
society 277
 
2.6%
none 277
 
2.6%
university 200
 
1.9%
and 160
 
1.5%
learning 153
 
1.5%
association 130
 
1.2%
data 129
 
1.2%
google 119
 
1.1%
Other values (281) 7673
73.1%
2025-01-03T09:56:21.525617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7767
 
9.0%
' 7347
 
8.5%
e 5359
 
6.2%
o 4991
 
5.8%
n 4946
 
5.7%
i 4735
 
5.5%
t 4197
 
4.8%
a 4126
 
4.8%
r 3168
 
3.7%
[ 2732
 
3.1%
Other values (51) 37389
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7767
 
9.0%
' 7347
 
8.5%
e 5359
 
6.2%
o 4991
 
5.8%
n 4946
 
5.7%
i 4735
 
5.5%
t 4197
 
4.8%
a 4126
 
4.8%
r 3168
 
3.7%
[ 2732
 
3.1%
Other values (51) 37389
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7767
 
9.0%
' 7347
 
8.5%
e 5359
 
6.2%
o 4991
 
5.8%
n 4946
 
5.7%
i 4735
 
5.5%
t 4197
 
4.8%
a 4126
 
4.8%
r 3168
 
3.7%
[ 2732
 
3.1%
Other values (51) 37389
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7767
 
9.0%
' 7347
 
8.5%
e 5359
 
6.2%
o 4991
 
5.8%
n 4946
 
5.7%
i 4735
 
5.5%
t 4197
 
4.8%
a 4126
 
4.8%
r 3168
 
3.7%
[ 2732
 
3.1%
Other values (51) 37389
43.1%

extra_curricular_organization_links
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.3%
Missing4903
Missing (%)64.2%
Memory size59.8 KiB
[None]
1155 
['N/A']
764 
[None, None]
476 
[None, None, None]
160 
['N/A', 'N/A']
 
85
Other values (4)
 
92

Length

Max length42
Median length36
Mean length9.1222548
Min length6

Characters and Unicode

Total characters24922
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row[None, None, None, None]
2nd row['N/A']
3rd row[None, None, None]
4th row[None, None, None]
5th row['N/A']

Common Values

ValueCountFrequency (%)
[None] 1155
 
15.1%
['N/A'] 764
 
10.0%
[None, None] 476
 
6.2%
[None, None, None] 160
 
2.1%
['N/A', 'N/A'] 85
 
1.1%
[None, None, None, None] 47
 
0.6%
[None, None, None, None, None, None] 24
 
0.3%
[None, None, None, None, None, None, None] 20
 
0.3%
[None, None, None, None, None] 1
 
< 0.1%
(Missing) 4903
64.2%

Length

2025-01-03T09:56:22.159019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T09:56:22.604770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
none 3064
76.6%
n/a 934
 
23.4%

Most occurring characters

ValueCountFrequency (%)
N 3998
16.0%
o 3064
12.3%
n 3064
12.3%
e 3064
12.3%
[ 2732
11.0%
] 2732
11.0%
' 1868
7.5%
, 1266
 
5.1%
1266
 
5.1%
/ 934
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3998
16.0%
o 3064
12.3%
n 3064
12.3%
e 3064
12.3%
[ 2732
11.0%
] 2732
11.0%
' 1868
7.5%
, 1266
 
5.1%
1266
 
5.1%
/ 934
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3998
16.0%
o 3064
12.3%
n 3064
12.3%
e 3064
12.3%
[ 2732
11.0%
] 2732
11.0%
' 1868
7.5%
, 1266
 
5.1%
1266
 
5.1%
/ 934
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3998
16.0%
o 3064
12.3%
n 3064
12.3%
e 3064
12.3%
[ 2732
11.0%
] 2732
11.0%
' 1868
7.5%
, 1266
 
5.1%
1266
 
5.1%
/ 934
 
3.7%

role_positions
Text

Missing 

Distinct91
Distinct (%)3.3%
Missing4903
Missing (%)64.2%
Memory size59.8 KiB
2025-01-03T09:56:23.269814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length236
Median length67
Mean length30.379209
Min length6

Characters and Unicode

Total characters82996
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row[None, None, None, None]
2nd row['N/A']
3rd row['Silver Medal For Economics Junior Award', 'Finalist at business solution PAN India', 'Winner and Runner-Up for 2018 and 2019 respectively']
4th row['Participant', 'Winner', 'Member']
5th row['Scholar']
ValueCountFrequency (%)
n/a 566
 
5.5%
data 422
 
4.1%
member 408
 
4.0%
python 369
 
3.6%
none 351
 
3.4%
participant 343
 
3.3%
with 277
 
2.7%
and 277
 
2.7%
learning 237
 
2.3%
science 204
 
2.0%
Other values (192) 6809
66.3%
2025-01-03T09:56:24.507950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7531
 
9.1%
' 7272
 
8.8%
e 6197
 
7.5%
n 5205
 
6.3%
a 4843
 
5.8%
t 4546
 
5.5%
i 4348
 
5.2%
r 3953
 
4.8%
o 3788
 
4.6%
[ 2732
 
3.3%
Other values (58) 32581
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7531
 
9.1%
' 7272
 
8.8%
e 6197
 
7.5%
n 5205
 
6.3%
a 4843
 
5.8%
t 4546
 
5.5%
i 4348
 
5.2%
r 3953
 
4.8%
o 3788
 
4.6%
[ 2732
 
3.3%
Other values (58) 32581
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7531
 
9.1%
' 7272
 
8.8%
e 6197
 
7.5%
n 5205
 
6.3%
a 4843
 
5.8%
t 4546
 
5.5%
i 4348
 
5.2%
r 3953
 
4.8%
o 3788
 
4.6%
[ 2732
 
3.3%
Other values (58) 32581
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7531
 
9.1%
' 7272
 
8.8%
e 6197
 
7.5%
n 5205
 
6.3%
a 4843
 
5.8%
t 4546
 
5.5%
i 4348
 
5.2%
r 3953
 
4.8%
o 3788
 
4.6%
[ 2732
 
3.3%
Other values (58) 32581
39.3%

languages
Categorical

High correlation  Missing 

Distinct18
Distinct (%)3.2%
Missing7066
Missing (%)92.5%
Memory size59.8 KiB
['English', 'Spanish']
131 
['Spanish']
72 
['Portuguese']
 
26
['English', 'Marathi', 'Hindi', 'Gujarati']
 
25
['English', 'French', 'Italian', 'Greek']
 
25
Other values (13)
290 

Length

Max length43
Median length34
Mean length24.936731
Min length10

Characters and Unicode

Total characters14189
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['Spanish']
2nd row['English', 'Spanish']
3rd row['English', 'Spanish']
4th row['English', 'Hebrew']
5th row['English', 'Hebrew']

Common Values

ValueCountFrequency (%)
['English', 'Spanish'] 131
 
1.7%
['Spanish'] 72
 
0.9%
['Portuguese'] 26
 
0.3%
['English', 'Marathi', 'Hindi', 'Gujarati'] 25
 
0.3%
['English', 'French', 'Italian', 'Greek'] 25
 
0.3%
['English', 'Swahili', 'Kalenjin'] 25
 
0.3%
['Spanish', 'English', 'Portuguese'] 24
 
0.3%
['English', 'Urdu', 'German'] 24
 
0.3%
['English', 'Chinese Mandarin'] 23
 
0.3%
['English', 'Arabic', 'Swedish'] 23
 
0.3%
Other values (8) 171
 
2.2%
(Missing) 7066
92.5%

Length

2025-01-03T09:56:25.061367image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 449
34.5%
spanish 247
19.0%
french 67
 
5.2%
portuguese 50
 
3.8%
hindi 47
 
3.6%
mandarin 44
 
3.4%
arabic 44
 
3.4%
marathi 25
 
1.9%
gujarati 25
 
1.9%
italian 25
 
1.9%
Other values (12) 277
21.3%

Most occurring characters

ValueCountFrequency (%)
' 2554
18.0%
i 1096
 
7.7%
n 1084
 
7.6%
h 881
 
6.2%
s 857
 
6.0%
a 756
 
5.3%
731
 
5.2%
, 708
 
5.0%
l 590
 
4.2%
] 569
 
4.0%
Other values (29) 4363
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 2554
18.0%
i 1096
 
7.7%
n 1084
 
7.6%
h 881
 
6.2%
s 857
 
6.0%
a 756
 
5.3%
731
 
5.2%
, 708
 
5.0%
l 590
 
4.2%
] 569
 
4.0%
Other values (29) 4363
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 2554
18.0%
i 1096
 
7.7%
n 1084
 
7.6%
h 881
 
6.2%
s 857
 
6.0%
a 756
 
5.3%
731
 
5.2%
, 708
 
5.0%
l 590
 
4.2%
] 569
 
4.0%
Other values (29) 4363
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 2554
18.0%
i 1096
 
7.7%
n 1084
 
7.6%
h 881
 
6.2%
s 857
 
6.0%
a 756
 
5.3%
731
 
5.2%
, 708
 
5.0%
l 590
 
4.2%
] 569
 
4.0%
Other values (29) 4363
30.7%

proficiency_levels
Categorical

High correlation  Missing 

Distinct22
Distinct (%)3.9%
Missing7066
Missing (%)92.5%
Memory size59.8 KiB
['Fluent', 'Fluent', 'Fluent']
69 
['Bilingual', 'Bilingual']
43 
['Fluent']
 
27
['fluent']
 
26
['N/A', 'N/A', 'N/A', 'N/A']
 
25
Other values (17)
379 

Length

Max length82
Median length36
Mean length30.801406
Min length7

Characters and Unicode

Total characters17526
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['N/A']
2nd row['Native Speaker', 'Native Speaker']
3rd row['Fluent', 'Decreased proficiency due to moderate use']
4th row['Fluent', 'Native']
5th row['Fluent', 'Native']

Common Values

ValueCountFrequency (%)
['Fluent', 'Fluent', 'Fluent'] 69
 
0.9%
['Bilingual', 'Bilingual'] 43
 
0.6%
['Fluent'] 27
 
0.4%
['fluent'] 26
 
0.3%
['N/A', 'N/A', 'N/A', 'N/A'] 25
 
0.3%
['Fluent', 'Fluent', 'Fluent', 'Fluent'] 25
 
0.3%
['Fluent', 'Fluent', 'GCSE level'] 24
 
0.3%
['Fluent - Full Knowledge', 'Knowledge, but taking classes to become more fluent'] 24
 
0.3%
['Bilingual', 'Bilingual', 'Fluent reading/writing'] 24
 
0.3%
['Advanced', 'Fluent'] 24
 
0.3%
Other values (12) 258
 
3.4%
(Missing) 7066
92.5%

Length

2025-01-03T09:56:25.672982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fluent 566
28.6%
n/a 234
 
11.8%
bilingual 134
 
6.8%
native 103
 
5.2%
speaker 60
 
3.0%
knowledge 48
 
2.4%
bi-lingual 46
 
2.3%
to 44
 
2.2%
and 42
 
2.1%
proficiency 42
 
2.1%
Other values (29) 660
33.4%

Most occurring characters

ValueCountFrequency (%)
' 2474
14.1%
e 1490
 
8.5%
1410
 
8.0%
n 1164
 
6.6%
l 1116
 
6.4%
t 953
 
5.4%
u 855
 
4.9%
i 794
 
4.5%
, 774
 
4.4%
a 585
 
3.3%
Other values (31) 5911
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 2474
14.1%
e 1490
 
8.5%
1410
 
8.0%
n 1164
 
6.6%
l 1116
 
6.4%
t 953
 
5.4%
u 855
 
4.9%
i 794
 
4.5%
, 774
 
4.4%
a 585
 
3.3%
Other values (31) 5911
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 2474
14.1%
e 1490
 
8.5%
1410
 
8.0%
n 1164
 
6.6%
l 1116
 
6.4%
t 953
 
5.4%
u 855
 
4.9%
i 794
 
4.5%
, 774
 
4.4%
a 585
 
3.3%
Other values (31) 5911
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 2474
14.1%
e 1490
 
8.5%
1410
 
8.0%
n 1164
 
6.6%
l 1116
 
6.4%
t 953
 
5.4%
u 855
 
4.9%
i 794
 
4.5%
, 774
 
4.4%
a 585
 
3.3%
Other values (31) 5911
33.7%

certification_providers
Categorical

High correlation  Missing 

Distinct49
Distinct (%)3.1%
Missing6048
Missing (%)79.2%
Memory size59.8 KiB
['N/A']
440 
['AWS']
 
47
['N/A', 'N/A']
 
45
['Tableau']
 
45
[None]
 
44
Other values (44)
966 

Length

Max length273
Median length76
Mean length26.040958
Min length6

Characters and Unicode

Total characters41327
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['Ohio Notary Public']
2nd row['Microsoft Certification']
3rd row['AIT']
4th row['Google Cloud']
5th row['N/A']

Common Values

ValueCountFrequency (%)
['N/A'] 440
 
5.8%
['AWS'] 47
 
0.6%
['N/A', 'N/A'] 45
 
0.6%
['Tableau'] 45
 
0.6%
[None] 44
 
0.6%
['PTA'] 25
 
0.3%
['New York State'] 25
 
0.3%
['IBM'] 25
 
0.3%
['State of Maine'] 25
 
0.3%
['Management Leadership Certification', 'Six Sigma Greenbelt certification'] 24
 
0.3%
Other values (39) 842
 
11.0%
(Missing) 6048
79.2%

Length

2025-01-03T09:56:26.222397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n/a 530
 
11.1%
certified 106
 
2.2%
aws 92
 
1.9%
scrum 90
 
1.9%
of 84
 
1.8%
state 71
 
1.5%
certification 71
 
1.5%
udemy 69
 
1.4%
linkedin 69
 
1.4%
six 64
 
1.3%
Other values (141) 3518
73.8%

Most occurring characters

ValueCountFrequency (%)
' 5200
 
12.6%
3177
 
7.7%
e 2732
 
6.6%
a 1887
 
4.6%
n 1799
 
4.4%
i 1799
 
4.4%
o 1772
 
4.3%
t 1694
 
4.1%
r 1690
 
4.1%
[ 1587
 
3.8%
Other values (50) 17990
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 5200
 
12.6%
3177
 
7.7%
e 2732
 
6.6%
a 1887
 
4.6%
n 1799
 
4.4%
i 1799
 
4.4%
o 1772
 
4.3%
t 1694
 
4.1%
r 1690
 
4.1%
[ 1587
 
3.8%
Other values (50) 17990
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 5200
 
12.6%
3177
 
7.7%
e 2732
 
6.6%
a 1887
 
4.6%
n 1799
 
4.4%
i 1799
 
4.4%
o 1772
 
4.3%
t 1694
 
4.1%
r 1690
 
4.1%
[ 1587
 
3.8%
Other values (50) 17990
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 5200
 
12.6%
3177
 
7.7%
e 2732
 
6.6%
a 1887
 
4.6%
n 1799
 
4.4%
i 1799
 
4.4%
o 1772
 
4.3%
t 1694
 
4.1%
r 1690
 
4.1%
[ 1587
 
3.8%
Other values (50) 17990
43.5%

certification_skills
Categorical

High correlation  Missing 

Distinct32
Distinct (%)2.0%
Missing6048
Missing (%)79.2%
Memory size59.8 KiB
[None]
666 
[None, None]
222 
[['Python']]
 
43
[None, None, None]
 
42
[['Python Programming'], ['Python Programming']]
 
25
Other values (27)
589 

Length

Max length272
Median length166
Mean length23.405167
Min length4

Characters and Unicode

Total characters37144
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[None]
2nd row[None]
3rd row[None]
4th row[None]
5th row[None]

Common Values

ValueCountFrequency (%)
[None] 666
 
8.7%
[None, None] 222
 
2.9%
[['Python']] 43
 
0.6%
[None, None, None] 42
 
0.6%
[['Python Programming'], ['Python Programming']] 25
 
0.3%
[['Data Science']] 25
 
0.3%
[['Tableau Visualization']] 24
 
0.3%
[['HIPAA Compliance']] 24
 
0.3%
[[]] 24
 
0.3%
[['Selenium']] 24
 
0.3%
Other values (22) 468
 
6.1%
(Missing) 6048
79.2%

Length

2025-01-03T09:56:26.538885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 1368
33.2%
python 249
 
6.0%
advanced 189
 
4.6%
intermediate 147
 
3.6%
data 91
 
2.2%
learning 90
 
2.2%
science 68
 
1.6%
sql 68
 
1.6%
and 67
 
1.6%
programming 50
 
1.2%
Other values (69) 1738
42.1%

Most occurring characters

ValueCountFrequency (%)
e 3460
 
9.3%
' 3294
 
8.9%
n 3193
 
8.6%
[ 2863
 
7.7%
] 2863
 
7.7%
2538
 
6.8%
o 2255
 
6.1%
N 1543
 
4.2%
, 1498
 
4.0%
a 1463
 
3.9%
Other values (49) 12174
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3460
 
9.3%
' 3294
 
8.9%
n 3193
 
8.6%
[ 2863
 
7.7%
] 2863
 
7.7%
2538
 
6.8%
o 2255
 
6.1%
N 1543
 
4.2%
, 1498
 
4.0%
a 1463
 
3.9%
Other values (49) 12174
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3460
 
9.3%
' 3294
 
8.9%
n 3193
 
8.6%
[ 2863
 
7.7%
] 2863
 
7.7%
2538
 
6.8%
o 2255
 
6.1%
N 1543
 
4.2%
, 1498
 
4.0%
a 1463
 
3.9%
Other values (49) 12174
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3460
 
9.3%
' 3294
 
8.9%
n 3193
 
8.6%
[ 2863
 
7.7%
] 2863
 
7.7%
2538
 
6.8%
o 2255
 
6.1%
N 1543
 
4.2%
, 1498
 
4.0%
a 1463
 
3.9%
Other values (49) 12174
32.8%

online_links
Categorical

High correlation  Missing 

Distinct7
Distinct (%)0.4%
Missing6048
Missing (%)79.2%
Memory size59.8 KiB
[None]
950 
[None, None]
325 
['N/A']
160 
[None, None, None]
 
64
[None, None, None, None]
 
44
Other values (2)
 
44

Length

Max length108
Median length6
Mean length10.097038
Min length6

Characters and Unicode

Total characters16024
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[None]
2nd row[None]
3rd row[None]
4th row[None]
5th row['N/A']

Common Values

ValueCountFrequency (%)
[None] 950
 
12.4%
[None, None] 325
 
4.3%
['N/A'] 160
 
2.1%
[None, None, None] 64
 
0.8%
[None, None, None, None] 44
 
0.6%
[None, None, None, None, None, None] 23
 
0.3%
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None] 21
 
0.3%
(Missing) 6048
79.2%

Length

2025-01-03T09:56:26.923903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-03T09:56:27.180949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
none 2484
93.9%
n/a 160
 
6.1%

Most occurring characters

ValueCountFrequency (%)
N 2644
16.5%
o 2484
15.5%
n 2484
15.5%
e 2484
15.5%
[ 1587
9.9%
] 1587
9.9%
, 1057
 
6.6%
1057
 
6.6%
' 320
 
2.0%
/ 160
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2644
16.5%
o 2484
15.5%
n 2484
15.5%
e 2484
15.5%
[ 1587
9.9%
] 1587
9.9%
, 1057
 
6.6%
1057
 
6.6%
' 320
 
2.0%
/ 160
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2644
16.5%
o 2484
15.5%
n 2484
15.5%
e 2484
15.5%
[ 1587
9.9%
] 1587
9.9%
, 1057
 
6.6%
1057
 
6.6%
' 320
 
2.0%
/ 160
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2644
16.5%
o 2484
15.5%
n 2484
15.5%
e 2484
15.5%
[ 1587
9.9%
] 1587
9.9%
, 1057
 
6.6%
1057
 
6.6%
' 320
 
2.0%
/ 160
 
1.0%

issue_dates
Categorical

High correlation  Missing 

Distinct30
Distinct (%)1.9%
Missing6048
Missing (%)79.2%
Memory size59.8 KiB
['N/A']
508 
[None]
221 
[None, None]
154 
['N/A', 'N/A']
105 
['N/A', 'N/A', 'N/A']
 
44
Other values (25)
555 

Length

Max length108
Median length36
Mean length12.31695
Min length6

Characters and Unicode

Total characters19547
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[None]
2nd row['May 2013']
3rd row[None]
4th row[None]
5th row['N/A']

Common Values

ValueCountFrequency (%)
['N/A'] 508
 
6.7%
[None] 221
 
2.9%
[None, None] 154
 
2.0%
['N/A', 'N/A'] 105
 
1.4%
['N/A', 'N/A', 'N/A'] 44
 
0.6%
['03/04/2015'] 25
 
0.3%
['1981'] 25
 
0.3%
['2011'] 25
 
0.3%
['2002'] 24
 
0.3%
['Feb 2017', 'Mar 2017'] 24
 
0.3%
Other values (20) 432
 
5.7%
(Missing) 6048
79.2%

Length

2025-01-03T09:56:27.555722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 1105
37.9%
n/a 869
29.8%
2013 110
 
3.8%
2021 69
 
2.4%
2015 64
 
2.2%
2011 48
 
1.6%
2017 48
 
1.6%
feb 47
 
1.6%
2014 46
 
1.6%
dec 45
 
1.5%
Other values (18) 462
15.9%

Most occurring characters

ValueCountFrequency (%)
' 3056
15.6%
N 2016
10.3%
[ 1587
 
8.1%
] 1587
 
8.1%
1326
 
6.8%
e 1258
 
6.4%
o 1170
 
6.0%
n 1105
 
5.7%
, 1057
 
5.4%
/ 939
 
4.8%
Other values (28) 4446
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 3056
15.6%
N 2016
10.3%
[ 1587
 
8.1%
] 1587
 
8.1%
1326
 
6.8%
e 1258
 
6.4%
o 1170
 
6.0%
n 1105
 
5.7%
, 1057
 
5.4%
/ 939
 
4.8%
Other values (28) 4446
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 3056
15.6%
N 2016
10.3%
[ 1587
 
8.1%
] 1587
 
8.1%
1326
 
6.8%
e 1258
 
6.4%
o 1170
 
6.0%
n 1105
 
5.7%
, 1057
 
5.4%
/ 939
 
4.8%
Other values (28) 4446
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 3056
15.6%
N 2016
10.3%
[ 1587
 
8.1%
] 1587
 
8.1%
1326
 
6.8%
e 1258
 
6.4%
o 1170
 
6.0%
n 1105
 
5.7%
, 1057
 
5.4%
/ 939
 
4.8%
Other values (28) 4446
22.7%

expiry_dates
Categorical

High correlation  Missing 

Distinct11
Distinct (%)0.7%
Missing6048
Missing (%)79.2%
Memory size59.8 KiB
[None]
814 
[None, None]
301 
['N/A']
227 
[None, None, None]
 
64
[None, None, None, None]
 
44
Other values (6)
137 

Length

Max length108
Median length6
Mean length10.647763
Min length6

Characters and Unicode

Total characters16898
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['February 15, 2021']
2nd row[None]
3rd row[None]
4th row[None]
5th row['N/A']

Common Values

ValueCountFrequency (%)
[None] 814
 
10.7%
[None, None] 301
 
3.9%
['N/A'] 227
 
3.0%
[None, None, None] 64
 
0.8%
[None, None, None, None] 44
 
0.6%
[None, 'Mar 2018'] 24
 
0.3%
['2015'] 24
 
0.3%
['February 15, 2021'] 23
 
0.3%
['May 2022', None, None, None, None, None] 23
 
0.3%
['Dec 2016'] 22
 
0.3%
(Missing) 6048
79.2%

Length

2025-01-03T09:56:27.818529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 2301
83.4%
n/a 227
 
8.2%
mar 24
 
0.9%
2018 24
 
0.9%
2015 24
 
0.9%
february 23
 
0.8%
15 23
 
0.8%
2021 23
 
0.8%
may 23
 
0.8%
2022 23
 
0.8%
Other values (2) 44
 
1.6%

Most occurring characters

ValueCountFrequency (%)
N 2528
15.0%
e 2346
13.9%
o 2301
13.6%
n 2301
13.6%
[ 1587
9.4%
] 1587
9.4%
1172
6.9%
, 1080
6.4%
' 686
 
4.1%
/ 227
 
1.3%
Other values (16) 1083
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2528
15.0%
e 2346
13.9%
o 2301
13.6%
n 2301
13.6%
[ 1587
9.4%
] 1587
9.4%
1172
6.9%
, 1080
6.4%
' 686
 
4.1%
/ 227
 
1.3%
Other values (16) 1083
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2528
15.0%
e 2346
13.9%
o 2301
13.6%
n 2301
13.6%
[ 1587
9.4%
] 1587
9.4%
1172
6.9%
, 1080
6.4%
' 686
 
4.1%
/ 227
 
1.3%
Other values (16) 1083
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2528
15.0%
e 2346
13.9%
o 2301
13.6%
n 2301
13.6%
[ 1587
9.4%
] 1587
9.4%
1172
6.9%
, 1080
6.4%
' 686
 
4.1%
/ 227
 
1.3%
Other values (16) 1083
6.4%

job_position_name
Categorical

High correlation  Uniform 

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.8 KiB
Database Administrator (DBA)
 
284
Full Stack Developer (Python,React js)
 
283
Mechanical Designer
 
281
Asst. Manager/ Manger (Administrative)
 
281
Machine Learning (ML) Engineer
 
280
Other values (23)
6226 

Length

Max length87
Median length38
Mean length29.481205
Min length10

Characters and Unicode

Total characters225089
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSenior Software Engineer
2nd rowMachine Learning (ML) Engineer
3rd rowExecutive/ Senior Executive- Trade Marketing, Hygiene Products
4th rowBusiness Development Executive
5th rowSenior iOS Engineer

Common Values

ValueCountFrequency (%)
Database Administrator (DBA) 284
 
3.7%
Full Stack Developer (Python,React js) 283
 
3.7%
Mechanical Designer 281
 
3.7%
Asst. Manager/ Manger (Administrative) 281
 
3.7%
Machine Learning (ML) Engineer 280
 
3.7%
Network Support Engineer 279
 
3.7%
Site Engineer 277
 
3.6%
Management Trainee - Mechanical 277
 
3.6%
Business Development Executive 276
 
3.6%
AI Engineer 276
 
3.6%
Other values (18) 4841
63.4%

Length

2025-01-03T09:56:28.121044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
engineer 3004
 
10.0%
2413
 
8.1%
executive 1896
 
6.3%
mechanical 827
 
2.8%
senior 822
 
2.7%
administrator 552
 
1.8%
manager 552
 
1.8%
management 548
 
1.8%
ai 546
 
1.8%
data 544
 
1.8%
Other values (61) 18213
60.9%

Most occurring characters

ValueCountFrequency (%)
e 28898
 
12.8%
22282
 
9.9%
n 19096
 
8.5%
i 16094
 
7.2%
r 14710
 
6.5%
a 12874
 
5.7%
t 12018
 
5.3%
c 7356
 
3.3%
g 6838
 
3.0%
o 6799
 
3.0%
Other values (43) 78124
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 225089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 28898
 
12.8%
22282
 
9.9%
n 19096
 
8.5%
i 16094
 
7.2%
r 14710
 
6.5%
a 12874
 
5.7%
t 12018
 
5.3%
c 7356
 
3.3%
g 6838
 
3.0%
o 6799
 
3.0%
Other values (43) 78124
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 225089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 28898
 
12.8%
22282
 
9.9%
n 19096
 
8.5%
i 16094
 
7.2%
r 14710
 
6.5%
a 12874
 
5.7%
t 12018
 
5.3%
c 7356
 
3.3%
g 6838
 
3.0%
o 6799
 
3.0%
Other values (43) 78124
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 225089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 28898
 
12.8%
22282
 
9.9%
n 19096
 
8.5%
i 16094
 
7.2%
r 14710
 
6.5%
a 12874
 
5.7%
t 12018
 
5.3%
c 7356
 
3.3%
g 6838
 
3.0%
o 6799
 
3.0%
Other values (43) 78124
34.7%

educationaL_requirements
Categorical

High correlation 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.8 KiB
Bachelor/Honors
1634 
Bachelor of Science (BSc) in Computer Science
558 
Bachelor of Science (BSc) in Computer Science & Engineering
540 
Bachelor of Science (BSc)
539 
Bachelor’s degree in Mechanical Engineering from a reputed institute.
 
281
Other values (15)
4083 

Length

Max length127
Median length75
Mean length51.716437
Min length15

Characters and Unicode

Total characters394855
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB.Sc in Computer Science & Engineering from a reputed university.
2nd rowM.Sc in Computer Science & Engineering or in any relevant discipline from a reputed University
3rd rowMaster of Business Administration (MBA)
4th rowBachelor/Honors
5th rowBachelor of Science (BSc) in Computer Science

Common Values

ValueCountFrequency (%)
Bachelor/Honors 1634
21.4%
Bachelor of Science (BSc) in Computer Science 558
 
7.3%
Bachelor of Science (BSc) in Computer Science & Engineering 540
 
7.1%
Bachelor of Science (BSc) 539
 
7.1%
Bachelor’s degree in Mechanical Engineering from a reputed institute. 281
 
3.7%
M.Sc in Computer Science & Engineering or in any relevant discipline from a reputed University 280
 
3.7%
Diploma, Bachelor/Honors 279
 
3.7%
Fresh graduates with a Bachelor’s degree in Mechanical Engineering or a related field. 277
 
3.6%
Bachelor of Science (BSc) in Civil Engineering 277
 
3.6%
Bachelors or Masters degree in Computer Science, Engineering, or a related field. 276
 
3.6%
Other values (10) 2694
35.3%

Length

2025-01-03T09:56:28.459218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 5459
 
9.9%
science 4110
 
7.5%
of 4054
 
7.4%
engineering 2743
 
5.0%
bachelor 2454
 
4.5%
bsc 2183
 
4.0%
a 1934
 
3.5%
computer 1927
 
3.5%
bachelor/honors 1913
 
3.5%
from 1635
 
3.0%
Other values (45) 26587
48.3%

Most occurring characters

ValueCountFrequency (%)
47101
 
11.9%
e 41213
 
10.4%
n 33191
 
8.4%
i 30523
 
7.7%
r 24562
 
6.2%
c 22396
 
5.7%
o 22056
 
5.6%
a 19827
 
5.0%
s 14854
 
3.8%
B 13807
 
3.5%
Other values (31) 125325
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 394855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47101
 
11.9%
e 41213
 
10.4%
n 33191
 
8.4%
i 30523
 
7.7%
r 24562
 
6.2%
c 22396
 
5.7%
o 22056
 
5.6%
a 19827
 
5.0%
s 14854
 
3.8%
B 13807
 
3.5%
Other values (31) 125325
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 394855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47101
 
11.9%
e 41213
 
10.4%
n 33191
 
8.4%
i 30523
 
7.7%
r 24562
 
6.2%
c 22396
 
5.7%
o 22056
 
5.6%
a 19827
 
5.0%
s 14854
 
3.8%
B 13807
 
3.5%
Other values (31) 125325
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 394855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47101
 
11.9%
e 41213
 
10.4%
n 33191
 
8.4%
i 30523
 
7.7%
r 24562
 
6.2%
c 22396
 
5.7%
o 22056
 
5.6%
a 19827
 
5.0%
s 14854
 
3.8%
B 13807
 
3.5%
Other values (31) 125325
31.7%

experiencere_requirement
Categorical

High correlation  Missing 

Distinct17
Distinct (%)0.3%
Missing1086
Missing (%)14.2%
Memory size59.8 KiB
At least 5 years
832 
At least 1 year
821 
At least 3 years
816 
1 to 3 years
548 
3 to 7 years
 
283
Other values (12)
3249 

Length

Max length18
Median length17
Mean length14.2153
Min length12

Characters and Unicode

Total characters93096
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAt least 1 year
2nd rowAt least 5 year(s)
3rd rowAt least 3 years
4th row1 to 3 years
5th rowAt least 4 years

Common Values

ValueCountFrequency (%)
At least 5 years 832
10.9%
At least 1 year 821
10.8%
At least 3 years 816
10.7%
1 to 3 years 548
 
7.2%
3 to 7 years 283
 
3.7%
At least 5 year(s) 280
 
3.7%
1 to 2 years 277
 
3.6%
At least 2 years 275
 
3.6%
At least 4 years 274
 
3.6%
5 to 10 years 272
 
3.6%
Other values (7) 1871
24.5%
(Missing) 1086
14.2%

Length

2025-01-03T09:56:28.743019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 5448
20.8%
at 3559
13.6%
least 3559
13.6%
to 2990
11.4%
5 2726
10.4%
3 1912
 
7.3%
1 1646
 
6.3%
2 1089
 
4.2%
year 821
 
3.1%
4 810
 
3.1%
Other values (6) 1636
 
6.2%

Most occurring characters

ValueCountFrequency (%)
19647
21.1%
e 10108
10.9%
a 10108
10.9%
t 10108
10.9%
s 9287
10.0%
y 6549
 
7.0%
r 6549
 
7.0%
A 3559
 
3.8%
l 3559
 
3.8%
o 2990
 
3.2%
Other values (11) 10632
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19647
21.1%
e 10108
10.9%
a 10108
10.9%
t 10108
10.9%
s 9287
10.0%
y 6549
 
7.0%
r 6549
 
7.0%
A 3559
 
3.8%
l 3559
 
3.8%
o 2990
 
3.2%
Other values (11) 10632
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19647
21.1%
e 10108
10.9%
a 10108
10.9%
t 10108
10.9%
s 9287
10.0%
y 6549
 
7.0%
r 6549
 
7.0%
A 3559
 
3.8%
l 3559
 
3.8%
o 2990
 
3.2%
Other values (11) 10632
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19647
21.1%
e 10108
10.9%
a 10108
10.9%
t 10108
10.9%
s 9287
10.0%
y 6549
 
7.0%
r 6549
 
7.0%
A 3559
 
3.8%
l 3559
 
3.8%
o 2990
 
3.2%
Other values (11) 10632
11.4%

age_requirement
Categorical

High correlation  Missing 

Distinct14
Distinct (%)0.3%
Missing3264
Missing (%)42.8%
Memory size59.8 KiB
Age 25 to 40 years
824 
Age at least 24 years
281 
Age at least 28 years
281 
Age 25 to 35 years
279 
Age at least 22 years
277 
Other values (9)
2429 

Length

Max length21
Median length18
Mean length19.00183
Min length18

Characters and Unicode

Total characters83057
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAge 22 to 30 years
2nd rowAge 25 to 40 years
3rd rowAge 22 to 30 years
4th rowAge at least 24 years
5th rowAge at least 28 years

Common Values

ValueCountFrequency (%)
Age 25 to 40 years 824
 
10.8%
Age at least 24 years 281
 
3.7%
Age at least 28 years 281
 
3.7%
Age 25 to 35 years 279
 
3.7%
Age at least 22 years 277
 
3.6%
Age 25 to 30 years 277
 
3.6%
Age 22 to 30 years 276
 
3.6%
Age 25 to 32 years 275
 
3.6%
Age 30 to 40 years 272
 
3.6%
Age 20 to 35 years 270
 
3.5%
Other values (4) 1059
 
13.9%
(Missing) 3264
42.8%

Length

2025-01-03T09:56:29.015329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
age 4371
20.0%
years 4371
20.0%
to 2736
12.5%
25 1655
 
7.6%
at 1635
 
7.5%
40 1361
 
6.2%
30 1358
 
6.2%
least 1109
 
5.1%
22 553
 
2.5%
35 549
 
2.5%
Other values (7) 2157
9.9%

Most occurring characters

ValueCountFrequency (%)
17484
21.1%
e 9851
11.9%
a 7115
8.6%
s 6006
 
7.2%
t 6006
 
7.2%
A 4371
 
5.3%
r 4371
 
5.3%
g 4371
 
5.3%
y 4371
 
5.3%
2 4129
 
5.0%
Other values (9) 14982
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17484
21.1%
e 9851
11.9%
a 7115
8.6%
s 6006
 
7.2%
t 6006
 
7.2%
A 4371
 
5.3%
r 4371
 
5.3%
g 4371
 
5.3%
y 4371
 
5.3%
2 4129
 
5.0%
Other values (9) 14982
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17484
21.1%
e 9851
11.9%
a 7115
8.6%
s 6006
 
7.2%
t 6006
 
7.2%
A 4371
 
5.3%
r 4371
 
5.3%
g 4371
 
5.3%
y 4371
 
5.3%
2 4129
 
5.0%
Other values (9) 14982
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17484
21.1%
e 9851
11.9%
a 7115
8.6%
s 6006
 
7.2%
t 6006
 
7.2%
A 4371
 
5.3%
r 4371
 
5.3%
g 4371
 
5.3%
y 4371
 
5.3%
2 4129
 
5.0%
Other values (9) 14982
18.0%

responsibilities.1
Categorical

High correlation  Uniform 

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.8 KiB
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting
 
284
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting
 
283
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation
 
281
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation
 
281
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
 
280
Other values (23)
6226 

Length

Max length587
Median length197
Mean length218.03039
Min length72

Characters and Unicode

Total characters1664662
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechnical Support Troubleshooting Collaboration Documentation System Monitoring Software Deployment Training & Mentorship Industry Trends Field Visits
2nd rowMachine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
3rd rowTrade Marketing Executive Brand Visibility, Sales Targets Field Marketing, Campaigns, Product Distribution Brand Head Excel, KPIs Tracking
4th rowApparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication
5th rowiOS Lifecycle Requirement Analysis Native Frameworks iOS Development API Integration Technical Communication UI Design Performance Optimization Feature Collaboration Bug Fixing Code Translation High-Performance Development Task Management Cross-Team Collaboration Code Quality

Common Values

ValueCountFrequency (%)
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting 284
 
3.7%
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting 283
 
3.7%
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation 281
 
3.7%
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation 281
 
3.7%
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis 280
 
3.7%
Mikrotik Router Configuration OLT Device Setup & Management Integration with Billing Software Network Monitoring Tools Integration Connectivity Troubleshooting Technical Support & Escalation Installation & Configuration GPON/EPON Expertise Cisco, OLT, MikroTik Knowledge 279
 
3.7%
Supervision Monitoring Construction Estimation Planning Material Management Project Coordination Quality Assurance Cost Control Inventory Operations Safety Error Escalation Miscellaneous Tasks 277
 
3.6%
Management Trainee Mechanical Systems Maintenance & Troubleshooting Performance Analysis Project Support Process Improvement Training & Development Administrative Support 277
 
3.6%
Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication 276
 
3.6%
Machine Learning Design Data Analysis Model Training AI Integration Innovation Cross-Functional Collaboration Model Deployment Documentation Analytical Skills Communication Team Collaboration 276
 
3.6%
Other values (18) 4841
63.4%

Length

2025-01-03T09:56:29.336918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11385
 
6.0%
management 5457
 
2.9%
development 4132
 
2.2%
design 3876
 
2.0%
data 3809
 
2.0%
collaboration 3574
 
1.9%
support 3024
 
1.6%
analysis 2752
 
1.4%
documentation 2469
 
1.3%
monitoring 2456
 
1.3%
Other values (323) 147881
77.5%

Most occurring characters

ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1664662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141492
 
8.5%
n 132606
 
8.0%
i 128303
 
7.7%
a 118492
 
7.1%
t 115658
 
6.9%
113410
 
6.8%
o 106615
 
6.4%
r 79793
 
4.8%
72520
 
4.4%
s 58738
 
3.5%
Other values (54) 597035
35.9%

skills_required
Categorical

High correlation  Missing  Uniform 

Distinct23
Distinct (%)0.4%
Missing1371
Missing (%)18.0%
Memory size59.8 KiB
ASP.NET MVC Strong understanding of database design Database Administrator (DBA) Database management Elasticsearch MongoDB MySQL database NoSQL database REDIS
 
284
AutoCAD Solidworks
 
281
•Administration •Health Safety and Environment •Safety and Security Management
 
281
CCNA (Cisco Certified Network Associate) GPON Hardware & Networking IIG ISP IT Enabled services OLT and ONU
 
279
Computer skill Good communication skills Mechanical Engineering Quick learner and hard working
 
277
Other values (18)
4862 

Length

Max length163
Median length63
Mean length71.475894
Min length11

Characters and Unicode

Total characters447725
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrand Promotion Campaign Management Field Supervision Merchandising promotional activities Trade Marketing
2nd rowFast typing skill IELTSInternet browsing & online work ability.
3rd rowiOS iOS App Developer iOS Application Development iOS Development Mobile apps Developer (iOS) Native IOS Swift (iOS) Swift UI
4th rowPython R or Java TensorFlow PyTorch Scikit-learn.
5th rowiOS iOS App Developer iOS Application Development iOS Development Mobile apps Developer (iOS) Native IOS Swift (iOS) Swift UI

Common Values

ValueCountFrequency (%)
ASP.NET MVC Strong understanding of database design Database Administrator (DBA) Database management Elasticsearch MongoDB MySQL database NoSQL database REDIS 284
 
3.7%
AutoCAD Solidworks 281
 
3.7%
•Administration •Health Safety and Environment •Safety and Security Management 281
 
3.7%
CCNA (Cisco Certified Network Associate) GPON Hardware & Networking IIG ISP IT Enabled services OLT and ONU 279
 
3.7%
Computer skill Good communication skills Mechanical Engineering Quick learner and hard working 277
 
3.6%
AutoCAD Communication and negotiation skills Internet MS Office 277
 
3.6%
Fast typing skill IELTSInternet browsing & online work ability. 276
 
3.6%
Python R or Java TensorFlow PyTorch Scikit-learn. 276
 
3.6%
Brand Promotion Campaign Management Field Supervision Merchandising promotional activities Trade Marketing 275
 
3.6%
Business Analysis Effective communication skills Java REST API Design Soft Skills Software Development 275
 
3.6%
Other values (13) 3483
45.6%
(Missing) 1371
 
18.0%

Length

2025-01-03T09:56:29.656236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 2727
 
4.5%
ios 1918
 
3.1%
database 1420
 
2.3%
management 1374
 
2.2%
skills 1104
 
1.8%
marketing 1067
 
1.7%
communication 829
 
1.4%
autocad 828
 
1.4%
823
 
1.3%
development 823
 
1.3%
Other values (148) 48232
78.9%

Most occurring characters

ValueCountFrequency (%)
33881
 
7.6%
e 33808
 
7.6%
a 31721
 
7.1%
n 30063
 
6.7%
i 29838
 
6.7%
o 24821
 
5.5%
t 24392
 
5.4%
21269
 
4.8%
r 19618
 
4.4%
s 15115
 
3.4%
Other values (48) 183199
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 447725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
33881
 
7.6%
e 33808
 
7.6%
a 31721
 
7.1%
n 30063
 
6.7%
i 29838
 
6.7%
o 24821
 
5.5%
t 24392
 
5.4%
21269
 
4.8%
r 19618
 
4.4%
s 15115
 
3.4%
Other values (48) 183199
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 447725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
33881
 
7.6%
e 33808
 
7.6%
a 31721
 
7.1%
n 30063
 
6.7%
i 29838
 
6.7%
o 24821
 
5.5%
t 24392
 
5.4%
21269
 
4.8%
r 19618
 
4.4%
s 15115
 
3.4%
Other values (48) 183199
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 447725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
33881
 
7.6%
e 33808
 
7.6%
a 31721
 
7.1%
n 30063
 
6.7%
i 29838
 
6.7%
o 24821
 
5.5%
t 24392
 
5.4%
21269
 
4.8%
r 19618
 
4.4%
s 15115
 
3.4%
Other values (48) 183199
40.9%

matched_score
Real number (ℝ)

Distinct214
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66066707
Minimum0
Maximum0.97
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size59.8 KiB
2025-01-03T09:56:29.969461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q10.57666667
median0.68333333
Q30.79333333
95-th percentile0.85
Maximum0.97
Range0.97
Interquartile range (IQR)0.21666667

Descriptive statistics

Standard deviation0.16740477
Coefficient of variation (CV)0.25338748
Kurtosis-0.17073382
Mean0.66066707
Median Absolute Deviation (MAD)0.11
Skewness-0.79900472
Sum5044.1931
Variance0.028024356
MonotonicityNot monotonic
2025-01-03T09:56:30.308640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85 1186
15.5%
0.65 1066
14.0%
0.716666667 513
 
6.7%
0.783333333 467
 
6.1%
0.683333333 456
 
6.0%
0.35 404
 
5.3%
0.75 353
 
4.6%
0.816666667 333
 
4.4%
0.45 187
 
2.4%
0.55 169
 
2.2%
Other values (204) 2501
32.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 2
< 0.1%
0.083333333 2
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.116666667 2
< 0.1%
0.133333333 3
< 0.1%
0.14 1
 
< 0.1%
0.146666667 1
 
< 0.1%
ValueCountFrequency (%)
0.97 1
 
< 0.1%
0.95 6
0.1%
0.943333333 2
 
< 0.1%
0.94 2
 
< 0.1%
0.936666667 7
0.1%
0.93 1
 
< 0.1%
0.926666667 6
0.1%
0.923333333 1
 
< 0.1%
0.92 2
 
< 0.1%
0.916666667 12
0.2%

Interactions

2025-01-03T09:55:53.398640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-01-03T09:56:30.568495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
addressage_requirementcertification_providerscertification_skillscompany_urlseducationaL_requirementsexperiencere_requirementexpiry_datesextra_curricular_organization_linksissue_dateslanguagesmatched_scoreonline_linksproficiency_levelsresponsibilitiesresponsibilities.1result_typesskills_requiredjob_position_name
address1.0000.0001.0000.9920.9870.0000.0000.9840.9880.9841.0000.2170.9841.0000.0000.0000.9860.0000.000
age_requirement0.0001.0000.0000.0000.0000.9200.8940.0000.0000.0000.0000.2110.0000.0001.0001.0000.0001.0001.000
certification_providers1.0000.0001.0000.8440.6940.0000.0000.9600.8090.8950.9970.2120.9251.0000.0000.0000.8360.0000.000
certification_skills0.9920.0000.8441.0000.5690.0000.0000.8670.6200.7140.9930.1420.9590.9900.0000.0000.7350.0000.000
company_urls0.9870.0000.6940.5691.0000.0000.0000.4110.5080.6750.8360.1020.4460.8970.0000.0000.3600.0000.000
educationaL_requirements0.0000.9200.0000.0000.0001.0000.8440.0000.0000.0000.0000.1920.0000.0000.9990.9990.0001.0000.999
experiencere_requirement0.0000.8940.0000.0000.0000.8441.0000.0000.0000.0000.0000.1850.0000.0000.9990.9990.0000.9990.999
expiry_dates0.9840.0000.9600.8670.4110.0000.0001.0000.6540.9190.9930.0960.9370.9900.0000.0000.5900.0000.000
extra_curricular_organization_links0.9880.0000.8090.6200.5080.0000.0000.6541.0000.7180.9890.0410.5510.9860.0000.0000.4210.0000.000
issue_dates0.9840.0000.8950.7140.6750.0000.0000.9190.7181.0000.9310.1520.9280.9930.0000.0000.7200.0000.000
languages1.0000.0000.9970.9930.8360.0000.0000.9930.9890.9311.0000.2110.9900.9160.0000.0000.8500.0000.000
matched_score0.2170.2110.2120.1420.1020.1920.1850.0960.0410.1520.2111.0000.1190.2140.2250.2250.1040.2270.225
online_links0.9840.0000.9250.9590.4460.0000.0000.9370.5510.9280.9900.1191.0000.9870.0000.0000.4930.0000.000
proficiency_levels1.0000.0001.0000.9900.8970.0000.0000.9900.9860.9930.9160.2140.9871.0000.0000.0000.9000.0000.000
responsibilities0.0001.0000.0000.0000.0000.9990.9990.0000.0000.0000.0000.2250.0000.0001.0001.0000.0001.0001.000
responsibilities.10.0001.0000.0000.0000.0000.9990.9990.0000.0000.0000.0000.2250.0000.0001.0001.0000.0001.0001.000
result_types0.9860.0000.8360.7350.3600.0000.0000.5900.4210.7200.8500.1040.4930.9000.0000.0001.0000.0000.000
skills_required0.0001.0000.0000.0000.0001.0000.9990.0000.0000.0000.0000.2270.0000.0001.0001.0000.0001.0001.000
job_position_name0.0001.0000.0000.0000.0000.9990.9990.0000.0000.0000.0000.2250.0000.0001.0001.0000.0001.0001.000

Missing values

2025-01-03T09:55:54.028744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-03T09:55:55.627474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-03T09:55:56.674408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

addresscareer_objectiveskillseducational_institution_namedegree_namespassing_yearseducational_resultsresult_typesmajor_field_of_studiesprofessional_company_namescompany_urlsstart_datesend_datesrelated_skils_in_jobpositionslocationsresponsibilitiesextra_curricular_activity_typesextra_curricular_organization_namesextra_curricular_organization_linksrole_positionslanguagesproficiency_levelscertification_providerscertification_skillsonline_linksissue_datesexpiry_datesjob_position_nameeducationaL_requirementsexperiencere_requirementage_requirementresponsibilities.1skills_requiredmatched_score
0NaNBig data analytics working and database warehouse manager with robust experience in handling all kinds of data. I have also used multiple cloud infrastructure services and am well acquainted with them. Currently in search of role that offers more of development.['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++', 'Data Structures', 'DBMS', 'RDBMS', 'Informatica', 'Talend', 'Amazon Redshift', 'Microsoft Azure']['The Amity School of Engineering & Technology (ASET), Noida']['B.Tech']['2019']['N/A'][None]['Electronics']['Coca-COla'][None]['Nov 2019']['Till Date'][['Big Data']]['Big Data Analyst']['N/A']Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\nNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSenior Software EngineerB.Sc in Computer Science & Engineering from a reputed university.At least 1 yearNaNTechnical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\nNaN0.850000
1NaNFresher looking to join as a data analyst and junior data scientist. Experienced in creating meaningful data dashboards and evaluation models.['Data Analysis', 'Data Analytics', 'Business Analysis', 'R', 'SAS', 'PowerBi', 'Tableau', 'Data Visualization', 'Business Analytics', 'Machine Learning']['Delhi University - Hansraj College', 'Delhi University - Hansraj College']['B.Sc (Maths)', 'M.Sc (Science) (Statistics)']['2015', '2018']['N/A', 'N/A']['N/A', 'N/A']['Mathematics', 'Statistics']['BIB Consultancy']['N/A']['Sep 2019']['Till Date'][['Data Analysis', 'Business Analysis', 'Machine Learning']]['Business Analyst']['N/A']Machine Learning Leadership\nCross-Functional Collaboration\nStrategy Development\nML/NLP Infrastructure\nPrototype Transformation\nML System Design\nAlgorithm Research\nApplication Development\nDataset Selection\nML Testing\nStatistical Analysis\nR&D in ML/NLP\nText Representation\nData Pipeline Design\nStatistical Data Analysis\nModel Training\nTeam Collaboration\nResearch Reporting\nAlgorithm AnalysisNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMachine Learning (ML) EngineerM.Sc in Computer Science & Engineering or in any relevant discipline from a reputed UniversityAt least 5 year(s)NaNMachine Learning Leadership\nCross-Functional Collaboration\nStrategy Development\nML/NLP Infrastructure\nPrototype Transformation\nML System Design\nAlgorithm Research\nApplication Development\nDataset Selection\nML Testing\nStatistical Analysis\nR&D in ML/NLP\nText Representation\nData Pipeline Design\nStatistical Data Analysis\nModel Training\nTeam Collaboration\nResearch Reporting\nAlgorithm AnalysisNaN0.750000
2NaNNaN['Software Development', 'Machine Learning', 'Deep Learning', 'Risk Assessment', 'Requirement Gathering', 'Application Support', 'JavaScript', 'Python', 'Docker', 'HTML', 'Hive', 'CSS', 'C', 'C++']['Birla Institute of Technology (BIT), Ranchi']['B.Tech']['2018']['N/A']['N/A']['Electronics/Telecommunication']['Axis Bank Limited']['N/A']['June 2018']['Till Date'][['Unified Payment Interface', 'Risk Prediction', 'Big Data', 'Spark', 'PySpark']]['Software Developer (Machine Learning Engineer)']['N/A']Trade Marketing Executive\nBrand Visibility, Sales Targets\nField Marketing, Campaigns, Product Distribution\nBrand Head\nExcel, KPIs TrackingNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNExecutive/ Senior Executive- Trade Marketing, Hygiene ProductsMaster of Business Administration (MBA)At least 3 yearsNaNTrade Marketing Executive\nBrand Visibility, Sales Targets\nField Marketing, Campaigns, Product Distribution\nBrand Head\nExcel, KPIs TrackingBrand Promotion\nCampaign Management\nField Supervision\nMerchandising\npromotional activities\nTrade Marketing0.416667
3NaNTo obtain a position in a fast-paced business office environment, demanding a strong organizational, technical, and interpersonal position utilizing my skills and attributes.['accounts payables', 'accounts receivables', 'Accounts Payable', 'Accounts Receivable', 'administrative functions', 'trial balance', 'banking', 'budget', 'bi', 'closing', 'Computer Applications', 'Credit', 'clients', 'Customer Service', 'data entry', 'delivery', 'driving', 'email', 'insurance', 'inventory', 'ledger', 'Access', 'Excel', 'Outlook', 'PowerPoint', 'Word', 'mortgage loan', 'Enterprise', 'policies', 'QuickBooks', 'Sales', 'sales reports', 'telecommunications', 'phone', 'workflow', 'written']['Martinez Adult Education, Business Training Center ï¼ City , State']['Computer Applications Specialist Certificate Program']['2008'][None][None]['Computer Applications']['Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'N/A'][None, None, None, None, None, None]['January 2011', 'January 2008', 'January 2006', 'January 2004', 'January 2001', 'N/A']['November 2015', 'January 2010', 'January 2008', 'January 2006', 'January 2004', None][['accounts receivables', 'banking', 'G/L Accounts', 'accounts payables', 'credit cards', 'reconcile', 'commission reports', 'credit checks', 'customer service', 'international travel'], ['data entry', 'accounts receivable', 'cash handling', 'customer communication', 'inventory reports', 'problem-solving'], ['mortgage processing', 'analytical aptitude', 'credit reports', 'customer communication'], ['commercial auto underwriting', 'data entry', 'application review', 'customer communication'], ['personal auto underwriting', 'data entry', 'policy review', 'customer service'], ['training', 'medical record review', 'data entry', 'document design', 'customer service', 'team performance']]['Accountant', 'Accounts Receivable Clerk', 'Mortgage Underwriter', 'Commercial Auto Underwriter', 'Personal Auto Underwriter', 'Claims Examiner']['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'N/A']Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor CommunicationNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNBusiness Development ExecutiveBachelor/Honors1 to 3 yearsAge 22 to 30 yearsApparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor CommunicationFast typing skill\nIELTSInternet browsing & online work ability.0.760000
4NaNProfessional accountant with an outstanding work ethic and integrity seeking to make a valuable contribution utilizing strong analytical, organizational, communication, and computer skills.['Analytical reasoning', 'Compliance testing knowledge', 'Effective time management', 'Public and private accounting', 'accounting', 'accounting systems', 'accounts payable', 'accounts receivable', 'administrative', 'AR', 'billing', 'closing', 'client', 'clients', 'documentation', 'financial', 'financial reports', 'preparation of financial reports', 'Preparation of financial statements', 'fixed assets', 'managing', 'month-end closing', 'policies', 'maintain records', 'reporting', 'Research', 'sales', 'tax', 'taxes', 'tax returns', 'annual reports', 'year-end']['Kent State University']['Bachelor of Business Administration'][None]['3.84'][None]['Accounting']['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name'][None, None, None, None, None]['January 2016', 'January 2016', 'January 2012', 'January 2009', 'January 2006']['Current', 'January 2016', 'January 2015', 'January 2011', 'January 2009'][['collections', 'accounts receivable', 'financial reports', 'AR aging', 'customer queries', 'sales and use tax audits'], ['financial statements', 'GAAP', 'asset', 'liability', 'capital account', 'accounting controls', 'audits'], ['sales tax', 'tax returns', 'business licenses', 'annual reports', 'tax audits'], ['financial reporting', 'fixed assets', 'sales tax', 'cash projections', 'general ledger accounting'], ['audit procedures', 'substantive tests', 'internal accounting', 'tests of compliance', 'audit programs']]['Staff Accountant', 'Senior Accountant', 'Tax Analyst', 'Staff Accountant II', 'Staff Auditor II']['City, State', 'City, State', 'City, State', 'City, State', 'City, State']iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality['Professional Organization', 'Honor Society', 'Honor Society', 'Honor Society']['Ohio Society of CPAs', 'Beta Alpha Psi', 'Golden Key International Honour Society', 'Beta Gamma Sigma'][None, None, None, None][None, None, None, None]NaNNaN['Ohio Notary Public'][None][None][None]['February 15, 2021']Senior iOS EngineerBachelor of Science (BSc) in Computer ScienceAt least 4 yearsNaNiOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode QualityiOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI0.650000
5NaNTo secure an IT specialist, desktop support, network administration, database administrator, technical support specialist or related position with a growing organization where my Microsoft certification, technical aptitude, networking, Windows and Mac OS, Apple and Android IOS, web development, application development, Linux, Microsoft applications, managing, testing, client support, help desk, technical support, troubleshooting, and leadership skills can benefit those who I work for as well as myself.['Microsoft Applications', 'Network Security', 'Networking', 'PC hardware and software installation, configuration, and troubleshooting', 'Remote Desktop and Help Desk Management', 'Verbal Communication', 'Technical Support', 'Team Leadership', 'Programming Languages', 'On-call tech support', 'Windows & Mac OS', 'Wiring/Wire Spicing: Cat3, Cat5, Cat5e, Coaxial', 'Management', 'VoIP, TCP/IP, IPSec, ATM, SS7, IPX, DNS, BIND, DHCP, HSRP and LAN/WAN architecture', 'Application Development', 'Voice Over IP Telephone', 'Inventory Management']['Glen Oaks High School', 'Glen Oaks High School']['Bachelor Degree', 'Associate Degree'][None, None][None, None][None, None]['Electronics and Communications Engineering Technology', 'Software Development']['N/A', 'Company Name', 'Company Name'][None, None, None]['August 2006', 'August 2013', 'July 2014']['January 2013', 'September 2014', 'Current'][['Microsoft Applications', 'Windows Applications', 'Mac OS and IOS', 'Network routers', 'Cisco ASA firewall', 'Juniper Net-screen', 'LANs, WANs', 'Cloud Experience'], ['Microsoft applications', 'Windows and Mac OS', 'Linux', 'Web Development'], ['Customer Support', 'Technical Support', 'Network Administration', 'Inventory Management']]['Engineering Systems Installer', 'IT Technician/QA Tester', 'Installation/Service Technician']['City, State', 'City, State', 'N/A']Machine Learning Design\nData Analysis\nModel Training\nAI Integration\nInnovation\nCross-Functional Collaboration\nModel Deployment\nDocumentation\nAnalytical Skills\nCommunication\nTeam CollaborationNaNNaNNaNNaNNaNNaN['Microsoft Certification'][None][None]['May 2013'][None]AI EngineerBachelors or Masters degree in Computer Science, Engineering, or a related field.NaNNaNMachine Learning Design\nData Analysis\nModel Training\nAI Integration\nInnovation\nCross-Functional Collaboration\nModel Deployment\nDocumentation\nAnalytical Skills\nCommunication\nTeam CollaborationPython\nR or Java\nTensorFlow\nPyTorch\nScikit-learn.0.850000
6NaNNaN['Machine Learning', 'Linear Regression', 'Ridge Regression', 'Lasso Regression', 'Tableau', 'Time Series Analysis']['DJR College and University']['B.Tech']['2020']['N/A']['N/A']['IT']['Remiro Amio']['N/A']['Jan 2019']['Sep 2019'][None]['Intern']['N/A']iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode QualityNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSenior iOS EngineerBachelor of Science (BSc) in Computer ScienceAt least 4 yearsNaNiOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode QualityiOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI0.650000
7NaNNaN['Maintenance', 'Corrective Maintenance', 'Documentation', 'Industrial Machinery', 'Preventive Maintenance', 'Sensors', 'Biotechnology', 'Electrical Mechanical', 'Estimation', 'Hydraulics', 'Mechanical Technician', 'Pneumatics', 'Project Manager', 'Sop', 'Manufacturing Process', 'Apqp', 'Assembly', 'Circuit Boards', 'Dmm', 'Electrical Test', 'Esd', 'First Article Inspection', 'Inspection', 'Medical Devices', 'Oscilloscope', 'Production Process', 'Schematic', 'Soldering', 'Surface Mount', 'Test Engineer', 'Through-hole', 'Wiring', 'Calibration', 'Control Systems', 'Packaging', 'Process Control', 'Sensor', 'Temperature', 'And Humidity', 'Control System Design', 'Electrical Engineer', 'Engineer', 'Entry Level', 'Ieee', 'Mechanical/electrical Engineer', 'Proactive', 'Self Motivated', 'Testing', 'Training', 'Electrical Engineering', 'Pr', 'Public Relations']['POLYTECHNIC UNIVERSITY OF PUERTO RICO']['Bachelor of Science']['2009']['2.50']['GPA']['Electrical Engineering']['Company Name', 'Company Name', 'Company Name'][None, None, None]['January 2013', 'January 2011', 'January 2005']['Current', 'January 2012', 'January 2011'][['Electrical Test', 'Soldering', 'Wiring', 'Troubleshooting'], ['Calibration', 'Troubleshooting', 'Preventive Maintenance', 'Installation'], ['Project Estimation', 'Corrective Maintenance', 'Preventive Maintenance', 'Installation']]['Engineering Technician', 'Instrument Technician', 'Project Manager Assistance']['City, State', 'City, State', 'City, State']iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode QualityNaNNaNNaNNaN['Spanish']['N/A']['AIT'][None][None][None][None]Senior iOS EngineerBachelor of Science (BSc) in Computer ScienceAt least 4 yearsNaNiOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode QualityiOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI0.650000
8NaNCertified Data analyst with a degree in Electronics Engineering, I have hands on experience in analyzing & interpreting data with good numerical accuracy.['Python', 'Machine Learning', 'MySQL', 'Data Mining', 'Deep Learning', 'Data Analysis', 'Computer Vision', 'Flask API', 'Predictive Modeling', 'AWS', 'Scikit-Learn', 'Numpy', 'Statistical Analysis', 'Multivariate Analysis', 'Decision Trees', 'Random Forest', 'Xgboost', 'NLP']['Nagpur University']['B.Tech/B.E.']['2019']['N/A']['N/A']['Electronics/Telecommunication']['AMZ Loans and Mortgages ERC Analytics']['N/A']['Jun 2019']['till date'][['Data Analysis', 'Employee Satisfaction', 'HR Collaboration']]['Associate Analyst']['N/A']Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceNaNNaNNaNNaNNaNNaN['Google Cloud'][None][None][None][None]Mechanical EngineerBachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical2 to 5 yearsAge 25 to 40 yearsMachinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceMaintenance and Troubleshooting\nMechanical0.550000
9NaNNaN['Django', 'Python', 'Relational databases', 'RestAPI', 'Github', 'Jira', 'PostgreSQL', 'Software development', 'Debugging', 'Machine learning', 'Natural language Processing', 'Artificial Intelligence', 'Data Analysis', 'Docker', 'Tornado', 'Software Developer', 'Project Management']['Dr. Virendra Swaroop Institute of Computer Studies, Kanpur', 'Pranveer Singh Institute of Technology, Kanpur']['BCA', 'MCA']['2016', '2019'][None, None][None, None]['Computers', 'Computers']['Daffodil Software Pvt Ltd'][None]['Jan 2019']['Till Date'][['Developing', 'designing', 'optimizing automation script', 'crons', 'managing database conflicts', 'executing solution']]['Software Developer']['N/A']Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor CommunicationNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNBusiness Development ExecutiveBachelor/Honors1 to 3 yearsAge 22 to 30 yearsApparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor CommunicationFast typing skill\nIELTSInternet browsing & online work ability.0.383333
addresscareer_objectiveskillseducational_institution_namedegree_namespassing_yearseducational_resultsresult_typesmajor_field_of_studiesprofessional_company_namescompany_urlsstart_datesend_datesrelated_skils_in_jobpositionslocationsresponsibilitiesextra_curricular_activity_typesextra_curricular_organization_namesextra_curricular_organization_linksrole_positionslanguagesproficiency_levelscertification_providerscertification_skillsonline_linksissue_datesexpiry_datesjob_position_nameeducationaL_requirementsexperiencere_requirementage_requirementresponsibilities.1skills_requiredmatched_score
7625NaNNaN['PLC', 'IEC 61131 (Ladder Logic, Functional Block Diagram, Structured Text, Instruction List.)', 'Java', 'C', 'Visual Basic', 'VHDL', 'PSpice', 'Assembly (Intel, Motorola, TI)', 'Labview', 'AutoCAD', 'Inventor', 'Matlab', 'Microsoft Office', 'PSIM', 'Easy Power', 'Xilinx ISE', 'Printed Circuit Board CAD (Protel)', 'Siemens Step 7', 'Wago CoDeSys', 'Allen Bradley RSLogix', 'ERP (Alliance, Global Shop, XA, SAP)', 'API', 'automation', 'budgeting', 'cabling', 'CAD', 'Conversion', 'client', 'clients', 'DC', 'Designing', 'flash', 'Functional', 'hardware design', 'HP', 'HVAC', 'instruction', 'Intel', 'microprocessor', 'Modeling', 'Motorola', 'Power distribution', 'power generation', 'processes', 'Programming', 'proposals', 'Renovation', 'renovations', 'safety', 'scheduling', 'schematics', 'Siemens', 'simulation', 'staff management', 'Structured', 'Supervising', 'switchgear', 'tender', 'troubleshoot', 'troubleshooting', 'validation']['University of Oklahoma']['Bachelor of Science']['2006']['3.73']['GPA']['Electrical and Computer Engineering']['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name'][None, None, None, None, None]['December 2014', 'May 2011', 'October 2008', 'August 2006', 'January 2005']['Current', 'December 2014', 'May 2011', 'October 2008', 'August 2006'][['PLC', 'ERP', 'AutoCAD', 'staff management', 'budgeting', 'scheduling'], ['Modeling', 'hardware design', 'simulation', 'verification', 'validation'], ['Designing', 'supervising', 'technical studies', 'trouble shooting'], ['power distribution', 'construction', 'HVAC'], ['design', 'implementation', 'microprocessor']]['Engineering Supervisor', 'Electrical Design Engineer', 'Project Engineer', 'Facility Engineer', 'Automation Engineer, Intern']['City , State', 'City , State', 'City , State', 'City , State', 'City , State']Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMechanical EngineerBachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical2 to 5 yearsAge 25 to 40 yearsMachinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceMaintenance and Troubleshooting\nMechanical0.826667
7626NaNMachine Learning Engineer seeking assignments Deep Learning, Reinforcement Learning, Tensorflow. Keras.['Artificial Intelligence', 'Deep Learning', 'Reinforcement Learning', 'Tensorflow Keras', 'Scikit learn', 'Numpy', 'Pandas', 'Matplotlib']['IIIT D&M Kancheepuram, Chennai']['B.Tech(IT)']['2019']['N/A']['N/A']['Information Technology']['Larsen & Toubro']['N/A']['Dec 2019']['Till Date'][['OCR', 'Machine Learning']]['Analyst Intern']['N/A']Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\n['Scholarship']['KSST']['N/A']['Scholar']NaNNaNNaNNaNNaNNaNNaNSenior Software EngineerB.Sc in Computer Science & Engineering from a reputed university.At least 1 yearNaNTechnical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\nNaN0.816667
7627NaNNaN['Data Science', 'Data Analysis', 'Machine Learning', 'Deep Learning', 'Statistical Analysis', 'Data Mining', 'Computer Vision', 'Algorithm Development', 'Linear Regression', 'Market Basket Analysis', 'Python', 'R', 'SQL', 'NoSQL']['Delhi College of Art', 'Aegis School of Business & Data Science']['B.Com', 'PG Diploma']['2017', '2019']['N/A', 'N/A']['N/A', 'N/A']['Commerce', 'Data Science and Business Analytics']['HP', 'Adnet Global'][None, None]['Feb 2020', 'Jan 2019']['till date', 'Dec 2019'][['Python'], ['Python', 'OpenCV', 'dlib']]['Lead Data Scientist', 'Data Scientist']['N/A', 'N/A']Administrative Support\nScheduling\nFiling & Documentation\nCommunication\nTeam Support\nEquipment Maintenance\nInformation Provision\nInventory Management\nTeam Collaboration\nOHS Policy Development\nSafety Advice\nRisk Assessment\nPolicy Review\nOHS Training\nSafety Inspections\nUnsafe Act Prevention\nIncident Investigation\nReport PreparationNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAsst. Manager/ Manger (Administrative)Bachelor/HonorsAt least 5 yearsAge at least 28 yearsAdministrative Support\nScheduling\nFiling & Documentation\nCommunication\nTeam Support\nEquipment Maintenance\nInformation Provision\nInventory Management\nTeam Collaboration\nOHS Policy Development\nSafety Advice\nRisk Assessment\nPolicy Review\nOHS Training\nSafety Inspections\nUnsafe Act Prevention\nIncident Investigation\nReport Preparation•Administration\n•Health Safety and Environment\n•Safety and Security Management0.650000
7628NaNNaN['Microsoft Office', 'Advanced Excel', 'PowerPoint', 'MS Access', 'Atlas', 'Windows 95 - XP', 'Harvard Graphics', 'QuickBooks', 'NGS Systems', 'People Soft', 'SAP', 'Quicken', 'Nanovision', '2-Tier', 'Phoenix (E1)', 'Business Objects', 'Account reconciliations', 'Accounting', 'Accounts payable', 'Accounts receivables', 'Accruals', 'Ad hoc', 'AP', 'Balance sheet', 'Balance', 'Bank reconciliations', 'Billing', 'Bookkeeping', 'Budgeting', 'Budget', 'Charts', 'Oral communication', 'CPA', 'Databases', 'Fluent in English', 'Senior management', 'Finance', 'Financial analysis', 'Financial reports', 'Financial Reporting', 'Financial statements', 'Fixed assets', 'Forecasting', 'General ledger', 'General ledger accounts', 'Government', 'Graphs', 'JDE', 'Lotus Notes', 'Presentations', 'QuickBooks', 'Quicken', 'Reporting', 'SAP', 'SOX compliance', 'Sarbanes Oxley', 'Spanish', 'Tax', 'Variance analysis', 'Excellent written']['Pepperdine University, Graziadio School of Business and Management', 'University of New Orleans']['Master of Business Administration', 'Bachelor of Science']['2008', '2004']['N/A', 'N/A']['N/A', 'N/A']['Business Development Strategy, Management', 'Finance Spanish']['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name'][None, None, None, None, None, None]['July 2015', 'July 2008', 'December 2007', 'October 2005', 'June 2005', 'September 2004']['Current', 'July 2015', 'June 2008', 'October 2007', 'August 2005', 'January 2005'][None, None, None, None, None, None]['Supervisor Accountant', 'Lead Accountant', 'Senior Accountant', 'Senior Financial Analyst', 'General Accountant', 'Accountant']['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'City, State']Database Design & Development\nSQL Query Optimization\nData Integrity & Security\nBI Solutions Development\nETL Process Implementation\nDatabase Maintenance\nBackup & Restore Management\nIndex Rebuilding & Performance Tuning\nSQL Server Clustering & High Availability\nSQL Server Replication\nHigh Availability Group Management\nDatabase Monitoring & TroubleshootingNaNNaNNaNNaN['English', 'Spanish']['Fluent', 'Decreased proficiency due to moderate use']NaNNaNNaNNaNNaNDatabase Administrator (DBA)Bachelor of Science (BSc) in Computer ScienceAt least 1 yearAge 25 to 40 yearsDatabase Design & Development\nSQL Query Optimization\nData Integrity & Security\nBI Solutions Development\nETL Process Implementation\nDatabase Maintenance\nBackup & Restore Management\nIndex Rebuilding & Performance Tuning\nSQL Server Clustering & High Availability\nSQL Server Replication\nHigh Availability Group Management\nDatabase Monitoring & TroubleshootingASP.NET MVC Strong understanding of database design\nDatabase Administrator (DBA)\nDatabase management\nElasticsearch\nMongoDB\nMySQL database\nNoSQL database\nREDIS0.676667
7629160 Bleecker Street, Apartment 4DW New York, NY 10012NaN['Game Theory', 'Bounded Rationality', 'Cryptography', 'Algorithms', 'Social Networks', 'Analysis of Algorithms', 'Combinatorial Optimizations']['Cornell University, Department of Computer Science', 'Tel Aviv University, Israel']['PhD Candidate, Computer Science', 'B.Sc., Double major in Computer Science and Management']['Present', '2008']['4.1', 'Summa cum laude']['GPA', 'N/A']['Computer Science', 'Computer Science and Management']['Google, AdX group', 'Google, Google Research', 'Intel, Mobile Wireless Group'][None, None, None]['Summer 2013', 'Summer 2012', '2007'][None, None, '2010'][['Product Development', 'Algorithm Design', 'Front-end Development'], ['Theoretical Research', 'Data Experiments'], ['Firmware Development', 'Design and Implementation', 'Integration Testing']]['Summer Intern', 'Summer Intern', 'FW Developer']['New York, NY', 'Mountain View, CA', 'Petach Tikva, Israel']Mushak Forms Maintenance\nVAT Software & MS Office Maintenance\nVAT Ledger Management\nVAT Challan Coordination\nLiaison with VAT Office\nVAT Documentation\nOther Assigned Tasks['Volunteering', 'Volunteering']['Intel Israel volunteer network', 'Tel Aviv University'][None, None]['Math tutor', 'CCNA teacher']['English', 'Hebrew']['Fluent', 'Native']NaNNaNNaNNaNNaNExecutive - VATBBA in Accounting and Finance1 to 3 yearsNaNMushak Forms Maintenance\nVAT Software & MS Office Maintenance\nVAT Ledger Management\nVAT Challan Coordination\nLiaison with VAT Office\nVAT Documentation\nOther Assigned TasksVAT and Tax0.650000
7630Trenton, NJDedicated and well-rounded software engineer with a passion for math, computer science, and elite education. Bringing 12+ years of teaching expertise to help Amazon's Career Fulfillment students feel the same joy and pride in my comprehensive JavaScript computer science program as my past 150+ high school students. Eager to teach the basics, get students the tools they need, and foster their passion to reach their goals.['HTML', 'CSS', 'JavaScript (Angular)', 'Python', 'Git', 'Algorithm Design', 'Technical Writing', 'Data Analysis', 'Interpersonal Communication', 'Patience']['Princeton International School of Mathematics and Science', 'New York University']['Master of Science', 'Bachelor of Science']['2015', '2012']['N/A', 'N/A'][None, None]['Computer Science', 'Computer Science']['Princeton International School of Mathematics and Science', 'HKA Enterprises', 'Academy of Mine']['N/A', 'N/A', 'N/A']['2017', '2014', '2012']['current', '2017', '2014'][['JavaScript', 'Teaching', 'Program Development'], ['Technical Support', 'Database Design', 'Software Testing'], ['Agile/Scrum', 'Code Writing', 'CICD']]['Computer Science Teacher', 'Software Engineer', 'Junior Software Engineer']['Princeton, NJ', 'Remote', 'Cherry Hill, NJ']15+ Years Banking Experience\nAudit/Inspection/ICC Leadership\nInternal Audit & Compliance\nRisk-Based Operational Reviews\nSystem-Based Audit Expertise\nKnowledge of Bangladeshi LawsNaNNaNNaNNaNNaNNaN['AWS', 'CSM'][None, None][None, None][None, None][None, None]Head of Internal Control & Compliance (ICC) - SEVP/DMDMasters, Master of Business Administration (MBA), Master of Business Management (MBM)At least 15 yearsAge at most 52 years15+ Years Banking Experience\nAudit/Inspection/ICC Leadership\nInternal Audit & Compliance\nRisk-Based Operational Reviews\nSystem-Based Audit Expertise\nKnowledge of Bangladeshi LawsAUDIT AND INSPECTION\nBanking\nInternal Audit0.643333
7631NaNWant to work as a Machine Learning Production Engineer.['Machine Learning Engineer', 'Data Analyst', 'Natural Language Processing', 'Deep Learning', 'Reinforcement Learning', 'Tensorflow', 'Keras', 'Scikit Learn', 'Numpy', 'Pandas', 'Matplotlib', 'Python']['AMIT, Bijnor', 'KVIT, Pilani']['B.Tech', 'M.Tech']['2017', 'N/A']['N/A', 'Gold Medalist']['N/A', 'N/A']['Electronics/Telecommunication', 'Advanced Analytics']['Larsen & Toubro'][None]['Jul 2019']['N/A'][None]['Analyst Intern']['N/A']Data Platform Design\nData Pipeline Development\nETL Processes\nSQL Query Optimization\nData Integration & Transformation\nData Modeling\nStakeholder Collaboration\nData Quality Monitoring\nContinuous Learning['Research Assistant & Teaching Assistant']['KVIT'][None]['N/A']NaNNaNNaNNaNNaNNaNNaNData EngineerBachelor of Science (BSc)5 to 8 yearsNaNData Platform Design\nData Pipeline Development\nETL Processes\nSQL Query Optimization\nData Integration & Transformation\nData Modeling\nStakeholder Collaboration\nData Quality Monitoring\nContinuous LearningAzure\nBig Data\nData Analytics\nETL Tools\nPower BI\nSQL0.650000
7632NaNSoftware developer focused in the areas of Machine learning application development. Motivated to learn, grow and excel my experience by challenging myself.['Software Engineer', 'Data Analyst', 'Machine Learning', 'Text Analytics', 'Software Development', 'Object Oriented Programming', 'Pandas', 'Numpy', 'Java', 'Python', 'SpringBoot', 'Laravel']['Dr. Jagjiban Rao Engineering College']['B.Tech']['2019'][None][None]['ECE']['KLP Technology Solutions'][None]['Jan 2019']['till date'][['Machine Learning', 'Software Engineering', 'Feature Engineering', 'Evaluation Methods']]['Software Engineer']['N/A']Mikrotik Router Configuration\nOLT Device Setup & Management\nIntegration with Billing Software\nNetwork Monitoring Tools Integration\nConnectivity Troubleshooting\nTechnical Support & Escalation\nInstallation & Configuration\nGPON/EPON Expertise\nCisco, OLT, MikroTik Knowledge['UI/UX Development']['OnDevice Machine Learning'][None][None]NaNNaNNaNNaNNaNNaNNaNNetwork Support EngineerDiploma, Bachelor/HonorsAt least 3 yearsAge 25 to 35 yearsMikrotik Router Configuration\nOLT Device Setup & Management\nIntegration with Billing Software\nNetwork Monitoring Tools Integration\nConnectivity Troubleshooting\nTechnical Support & Escalation\nInstallation & Configuration\nGPON/EPON Expertise\nCisco, OLT, MikroTik KnowledgeCCNA (Cisco Certified Network Associate)\nGPON\nHardware & Networking\nIIG\nISP\nIT Enabled services\nOLT and ONU0.760000
7633NaNNaN['Accounting', 'Accounting Software', 'Accounts Payables', 'accounts payable', 'Accounts Receivables', 'accrual', 'administrative', 'administrative support', 'Trial Balance', 'balance', 'Balance Sheet', 'bank reconciliation', 'Bank Reconciliation', 'Bookkeeping', 'book', 'c', 'Driving License', 'Cash Management', 'closing', 'computer peripherals', 'Credit', 'Clients', 'Data Entry', 'email', 'English', 'ERP', 'Finance', 'Financials', 'Financial', 'Fixed Assets', 'Funds', 'General Ledger', 'Hindi', 'Insurance', 'Internet applications', 'Invoicing', 'Languages', 'law', 'legal', 'letters', 'Managing', 'Excel', 'MS Office applications', 'office', 'Outlook', 'PowerPoint', 'Word', 'MIS', 'MYOB', 'negotiating', 'Payroll', 'policies', 'Purchasing', 'QuickBooks', 'Sales', 'sales analysis', 'Secretarial', 'Sound', 'strategy', 'supervision', 'typing speed', 'Xpert']['University of Kerala', 'Mahatma Gandhi University']['M.Com (Master of Commerce)', 'B.Com (Bachelor of Commerce)']['N/A', 'N/A']['N/A', 'N/A']['N/A', 'N/A']['N/A', 'N/A']['Company Name', 'Company Name', 'Company Name', 'Company Name'][None, None, None, None]['October 2012', 'April 2011', 'July 2008', 'August 2005']['October 2014', 'October 2012', 'April 2011', 'July 2008'][['Accounts Payables', 'Financial Transactions', 'Accounting'], ['Accounts Payables', 'Bank Reconciliation', 'Financial Reports'], ['Invoicing', 'Accounts Receivables', 'Payroll'], ['Bookkeeping', 'Bank Reconciliation', 'Sales']]['Accountant', 'Accountant - Payables / Accounting Clerk', 'Junior Accountant', 'Accountant cum Secretary']['City', 'City', 'City', 'City']Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceNaNNaNNaNNaN['English', 'Hindi', 'Malayalam']['N/A', 'N/A', 'N/A']NaNNaNNaNNaNNaNMechanical EngineerBachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical2 to 5 yearsAge 25 to 40 yearsMachinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceMaintenance and Troubleshooting\nMechanical0.350000
7634NaNNaN['Electronic & Mechanical Technology', 'Maintenance Management', 'Project Management', 'Program Management', 'Lean/six sigma principles', 'Mechanical Component Troubleshooting', 'Quality Assurance/Control', 'Material Management', 'Staff Development/Leadership', 'Technical Interface', 'Safety Compliance', 'Microsoft Office', 'Microsoft Project', 'Microsoft Access', 'SAP', 'SharePoint']['Eastern New Mexico University', 'Embry-Riddle Aeronautical University', 'Enterprise Community College']['MBA', 'Bachelor of Science', 'Associate of Science']['2015', '2008', '2004']['N/A', 'N/A', 'N/A']['N/A', 'N/A', 'N/A']['N/A', 'Professional Aeronautics', 'Airframe & Power Plant Technology']['Company Name', 'Company Name', 'Company Name'][None, None, None]['02/2018', '08/2011', '03/2001']['Current', '02/2018', 'Current'][['Project Management', 'Engineering', 'Technical Coordination'], ['Aircraft Maintenance', 'Technical Support', 'Engineering Change Management', 'Project Planning'], ['Aircraft Maintenance', 'Technical Supervision', 'Quality Control', 'System Calibration']]['Engineering Project Manager III', 'Field Engineer/Maintenance Support Engineer', 'Aircraft Mechanic / Electrician / Avionics Technician']['City, State', 'City, State', 'State']Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceNaNNaNNaNNaNNaNNaN['FCC', 'A&P'][None, None][None, None]['N/A', 'N/A'][None, None]Mechanical EngineerBachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical2 to 5 yearsAge 25 to 40 yearsMachinery Maintenance\nTroubleshooting\nReport Preparation\nLog MaintenanceMaintenance and Troubleshooting\nMechanical0.716667